Delft University of Technology (TU Delft), Netherlands invites online Application for number of Fully Funded PhD Degree at various Departments. We are providing a list of Fully Funded PhD Programs available at Delft University of Technology (TU Delft), Netherlands.
Eligible candidate may Apply as soon as possible.
(01) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Design of Emerging Neuromorphic Architectures Based on the Human Neocortex
The field of neuromorphic engineering aims at replicating the brain’s key organizing principles in custom silicon toward order-of-magnitude efficiency improvements compared to current processor architectures. Based on this promise, neuromorphic engineering is now included in worldwide research roadmaps, has seen a x10 increase in yearly research output over the last decade, and fuels interest from large industrial players as well as a flourishing landscape of new startups.
Deadline : Open until filled
(02) PhD Degree – Fully Funded
PhD position summary/title: PhD Position High-Fidelity Modelling of Intensified Hydrogen Production from Biomass
Anthropogenic activities have broken the natural carbon cycle, and it is becoming urgent to find alternative, more sustainable routes to energy conversion. Hydrogen is a very versatile energy carrier, typically used as an intermediate before being further valorized via subsequent catalytic approaches and intensified reactor technologies. Production of hydrogen from thermochemical processes involving biomass holds great promise to help with the energy transition from fossil fuels.
Gasification is a conversion path which proceeds at high temperatures (> 800°C) in an oxygen-starved environment, to produce syngas from a feedstock like biomass. Many such processes are being experimentally investigated, but variability in yields and overall reactor performances (including fouling) remain a hindrance to scale-up. A viable technology will require for us to gain a better understanding of the mechanisms at the core of the reactors: coupling between fluid flow and chemistry, coupling between solid phase and fluid flow, solid phase decomposition, pollutant formation (including tars), impact of design choices and biomass type, etc. Classical, experimentally based investigation methods, often fall short of providing sufficient details for a deep understanding because of the extreme conditions. Numerical simulations can prove very valuable to help at the design stage and to develop efficient control strategies; but they need to be of sufficiently high fidelity to gain useful input. High-fidelity modelling, while still expensive, is considered more and more due to the continuous development of computational resources. The advent of GPUs, in particular, has recently enabled realistic multiscale simulations of processes (https://mfix.netl.doe.gov/research/applications/) and combustion devices (https://youtu.be/XNKDs0mkym0?feature=shared). Indeed, CFD methods are at a more advanced stage in other engineering fields bearing similitudes with the complex multiphase reacting flows at the core of gasifiers.
Deadline : 3 July 2024
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(03) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Distributed & Adaptive radar for Drone Monitoring
We seek a motivated PhD student to work on a 4-year NWO funded project called DARE (Distributed and Adaptive Radar for Enhanced Sensing and Classification).
The goal of this project is to work towards transitioning radar from a conventional stand-alone sensor to an intelligent and spatially distributed network of cooperative nodes. The spatially distributed aspect will provide information from many partial viewpoints to reconstruct more detailed 3D signatures of the observed scenarios. The intelligent aspect will enable the radar to adapt its parameters and processing to the changes in objects’ behavior and environment, like in a sort of “chess game”. Hence, the intended scientific breakthrough is to formulate, implement, and validate the ‘distributed radar brain’ needed to establish and support this new sensing approach, combining spatially-distributed with adaptive capabilities in radar classification.
Specifically, in this project we work on the problem of monitoring multiple drones that might (or not) act as a coordinated swarm, which is a very timely problem for safety of public spaces and national assets such as airports or stadiums, not just in conflict scenarios but also for every-day monitoring. This problem is scientifically challenging, as drones are relatively small and highly manoeuvrable objects, not easy to track by means of radar sensing especially in an urban environment, and even less easy to understand their intent (e.g., whether they are just accidentally flown where they should not, or if there is an actual malicious intent). Distributed and adaptive radar techniques have the potential to improve our capabilities to monitor these situations, and we seek motivated students to take up this challenge.
Deadline : July 1, 2024
(04) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Distributed & Adaptive Radar for Human wellbeing Monitoring
We seek a motivated PhD student to work on a 4-year NWO funded project called DARE (Distributed and Adaptive Radar for Enhanced Sensing and Classification).
The goal of this project is to work towards transitioning radar from a conventional stand-alone sensor to an intelligent and spatially distributed network of cooperative nodes. The spatially distributed aspect will provide information from many partial viewpoints to reconstruct more detailed 3D signatures of the observed scenarios. The intelligent aspect will enable the radar to adapt its parameters and processing to the changes in objects’ behavior and environment, like in a sort of “chess game”. Hence, the intended scientific breakthrough is to formulate, implement, and validate the ‘distributed radar brain’ needed to establish and support this new sensing approach, combining spatially-distributed with adaptive capabilities in radar classification.
Specifically, in this project we work on the problem of observing people and supporting their wellbeing. This is primarily looking at the context of monitoring drivers’ and passengers’ conditions in the cabin of future smart cars, as well as more in general the health condition of vulnerable individuals in an indoor environment (e.g., people with health conditions living alone who might suffer from accidents such as falls). This problem is scientifically challenging, as 1) indoor environments such as vehicle cabins present a lot of clutter and multipath masking the signature of the person or people to observe; and 2) the human body is a rather complicated object to observe with radar, as we want to observe reliably the very small movements associated to vital signs (e.g., heartbeat) and the larger movements such as a fall while walking. Distributed and adaptive radar techniques have the potential to improve our capabilities to monitor these situations, and we seek motivated students to take up this challenge.
Deadline : July 1, 2024
(05) PhD Degree – Fully Funded
PhD position summary/title: PhD position Quantitative ultrasound imaging inside and behind bone
Our main goal is to unlock ultrasound imaging inside organs that are still inaccessible to existing clinical ultrasound scanners, in particular bones and the adult human brain. Our ambition is to develop operator-independent approaches that generate accurate anatomical volumetric images and quantitative biomarkers, in particular for the characterization of tissue composition or microstructure and blood flow quantification. We recently unlocked 2D ultrasound imaging inside human long bones (https://doi.org/10.1007/978-3-030-91979-5_10) and started the translation of our approaches to transcranial ultrasound (https://doi.org/10.1109/TUFFC.2022.3148121).
Optimal imaging inside and behind bone requires to take into account the 3D challenging wave physics imposed by bone tissue: wave refraction, wave speed anisotropy, wave mode conversion, multiple scattering. Our first study on patients will start soon, to validate the detection and sizing of hypervascular lesions inside bones. We are presently recruiting a PhD student to pursue a four year doctorate in our lab.
The expected starting date for this position is the 1st of October 2024. The PhD student will develop innovative ultrasound imaging approaches from the bottom up: starting from wave physics in complex media to clinical translation. In particular, the PhD student will develop quantitative approaches exploiting elastic waves and 4D ultrasound imaging strategies with matrix array transducers. Next, in close collaboration with clinicians, these innovative approaches will be tailored to answer specific needs in clinical research and clinical practice, and evaluated in vivo on human volunteers. The successful candidate will be involved in ongoing research projects at the lab. The PhD student will have the opportunity to teach and take mentoring responsibility for students at the bachelor’s and master’s levels.
Deadline : 01 July 2024
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(06) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Particle Free Contactless Wafer Handling
The fast and accurate handling, transportation, and positioning of thin, sensitive substrates, such as Si-wafers, solar cells, and flat panel display glass panels are all core operations in production and manufacturing systems in high-tech industry, and substrate handling systems can be found everywhere in this industry.
Every mechanical contact between system components that are in relative movement increases the risk of wear of these components and the release of wear particles in the system, and every mechanical contact between handling system and substrate increases the risk of contamination, damage, or even breakage of said substrate, all of which need to be avoided. And yet, in current substrate handling systems found in industry mechanical contact is prevalent, with the unavoidable resulting contamination of the substrates that are being handled.
In this research project we will develop new concepts for future handling systems for Si-wafers in close collaboration with our industrial partner, VDL-ETG, an important OEM supplier in the world-leading Dutch high-tech industry. These systems will handle Si-wafers without mechanical contact whenever possible, and when unavoidable, make sure that the mechanical contact is without damage or contamination. At TU Delft, a novel, air bearing based contactless handling concept is being developed in which a thin pressurized air film carries, moves and accurately positions substrates relative to the carrying system. First results show the potential to carry, transport and position thin substrates without any mechanical contact.
In this project you will study and develop this new concept to be used in an innovative, new, contactless end-effector for a wafer handling SCARA robot. Additional challenges are to add the thermal conditioning of the wafer and the pre-alignment of the wafer to the features of this end-effector. Key steps in this research are the understanding and optimization of the air flow in the thin lubricating film between system and substrate to generate the required viscous traction on the substrate and thus control its position, while at the same time controlling the viscous heat loss.
Deadline :30 June 2024
(07) PhD Degree – Fully Funded
PhD position summary/title: PhD Networks of Care: The Architecture of Future Healthcare Environments
In a world increasingly driven by data, healthcare facilities are undergoing profound changes, embracing new technologies to reinvent how they deliver acute care and interact with other healthcare providers. This shift enables healthcare institutions to focus on their core competencies while delegating non-essential tasks to specialized entities. As wearables and sensor networks become more prevalent, the traditional model of hospitals as standalone care providers is evolving into a broader, interconnected network that integrates personal wearables, smart home systems, and other healthcare technologies.
While these data technologies primarily focus on the human body, the evolution of healthcare environments must also be rooted in innovative architectural design. For instance, integrating advanced sensor networks not only enhances operational efficiency but also significantly improves the overall patient experience by creating more responsive and human-centered environments. In this dynamic landscape, the role of architecture is more critical than ever, adapting to current needs while anticipating future shifts.
The Design, Data, and Society Group at the Department of Architecture at TU Delft invites applications for a fully funded PhD position focused on the architectural aspects of this new era of connected care. This project will critically reflect and explore the relation between design, artificial intelligence and data infrastructure in healthcare architecture.
Deadline : Open until filled
(08) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Multivariate Dependence Modelling and Statistical Machine Learning Algorithms for Patient Risk Profiling
The Applied Probability research group within the Delft Institute of Applied Mathematics at TU Delft (see https://www.tudelft.nl/ewi/over-de-faculteit/afdelingen/applied-mathematics ) is offering a full-time PhD position in the area of Multivariate dependence modelling and statistical machine learning algorithms for patient risk profiling.
In partnership with RECENTRE (see https://www.4tu.nl/recentre/), a project dedicated to helping people take charge of their health, we’re working on mathematical approaches to quantify health-related risks. As a PhD researcher, you’ll be at the forefront of this exciting journey, focusing on multivariate dependence models and statistical machine learning algorithms at its core to create personalized individual risk profiles. Along with developing novel methods and investigating their theoretical properties, you will also apply your methods and validate them using the latest available datasets.
The project will be co-supervised by Özge Şahin and Tina Nane in Applied Probability from TU Delft and Annemieke Witteveen in Biomedical Signals and Systems from the University of Twente.
Besides carrying out mathematical research and working on scientific articles, you will be expected to actively participate in seminars and discussions with colleagues from the department, as well as to present the findings at (inter)national conferences or workshops. Furthermore, you are supposed to carry out minor teaching tasks (at most 15% of the contract time). Teaching in English will be possible. Communication will be in English. If desired, there is the opportunity to attend Dutch language courses offered by the Language Center for Languages and Academic Skills at TU Delft, see https://www.tudelft.nl/en/tpm/itav/education/dutch-courses.
Deadline : Open until filled
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(09) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Digitalisation of Project Work
Digitalisation is affecting all aspects of our lives through new technologies, processes, tasks and new forms of interactions. This research project focuses on how digitalisation affects project work and teams in the civil engineering field. The position will be embedded in the TU Delft, Faculty of Civil Engineering & Goesciences, Section of Integral Design and Management and will be positioned at the intersection of project management, digitalisation and social science.
This project aims to increase our understanding of how projects can be delivered leveraging the opportunities of digitalisation. Focusing on the people aspect of projects and the increased societal need for inclusive teamwork, wellbeing and job satisfaction, this project will focus on exploring ways that digitally-enabled projects contribute to social sustainability and a better world. We are seeking a highly motivated PhD candidate to join our research team, focusing on the research in the area of project management, digitalisation and wellbeing integrating social science methods and systems thinking. This PhD project presents a unique opportunity to research the balance of people and technology for project work which is relevant to solving grand challenges in the 21st century.
Deadline : 16 June 2024
(10) PhD Degree – Fully Funded
PhD position summary/title: PhD 3D Super-resolution microscopy
The PhD position is part of research of prof. Bernd Rieger and prof. Sjoerd Stallinga, in which we target 3D super-resolution microscopy by the “re-scan confocal” technique. Here the confocal spot that is scanned through the sample is re-imaged onto a conventional camera, enabling advances in resolution and signal-to-noise ratio.
We strive for improving this technique with optical engineering of the scan spots and/or scan patterns, by extending the technique to full 3D microscopy, and by applying it to single-molecule localization. The project builds on the lab’s track-record in the field of computational imaging techniques for super-resolution microscopy.
The project will run in collaboration with the company Confocal.nl that brings the re-scan confocal technique to microscopy users, and is part of a bigger consortium on 3D Nanoscale Imaging in which different academic groups run research collaborations with companies in this area.
Deadline : 15 June 2024
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(11) PhD Degree – Fully Funded
PhD position summary/title: PhD position – Cell Line Engineering for Cellular Agriculture
As part of this new and exciting research program at TU Delft, we are seeking to hire a PhD student to contribute to the development of CA, focusing on the engineering of mammalian cell lines with desirable properties for upscaled cultivated meat production. Such properties include immortalisation, capacity for extreme-density cultivation, resistance to cellular stresses (for example, shear stress induced by agitation), and proliferation in minimal media (with respect to both growth factors, and basic nutrient sources).
The PhD candidate will design and optimise strategies for the engineering of such phenotypes into mammalian cells from agriculturally relevant species, using a variety of genetic modification and gene editing tools, including CRISPR/Cas9. Initial projects might include the development of an immortal and apoptosis-resistant muscle cell line. Attention will be paid to the methods used, to ensure industry-relevance in a complex regulatory landscape. For example, precision nuclease-, base- and prime-editing techniques will be employed where appropriate. The candidate will also combine gene edits/modifications to produce candidate cell lines for subsequent screening in upscaled culture systems, in collaboration with other members of the group and department. This work will contribute to the development of integral process concepts for CA products based on mammalian cell culture.
Deadline : 15 June 2024
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(12) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Responsible Innovation Ecosystems: Shaping Technology for the Public Good
What does it mean to organise the ecosystem of businesses and organisations that are developing quantum technology such that this technology realises the public good? Innovation ecosystems tend to have technology development and economic success as the end goals of innovation governance. Less is known about the governance of innovation ecosystems with respect to ethical and societal concerns. Quantum Delta NL is the Dutch national quantum ecosystem that aims to deliver on economic competitiveness and on societal benefits of quantum technology development or what is termed ‘quantum for good’.In order to achieve ‘quantum for good’, the ecosystem will need to align its processes and outcomes with societal needs and expectations such that quantum development in and by the ecosystem is acceptable, desirable and sustainable.
The governance of innovation ecosystems for the public good may be seen as the next step in responsible research and innovation of technology. The goal of this PhD position is to take this next step toward responsible innovation ecosystem development by exploring the conditions and interventions necessary to shape a quantum ecosystem in ways that promote ‘quantum for good’.
Deadline : 15 June 2024
(13) PhD Degree – Fully Funded
PhD position summary/title: PhD position Thermo-physical properties of nuclear fuels for Molten Salt Reactors
The molten salt reactor (MSR) was selected as one of the promising designs by the International Generation IV International Forum for the next generation of nuclear reactors. Running on a liquid molten salt fuel as opposed to the current generation of nuclear reactors, the Molten Salt Reactor technology provides a safe and truly innovative concept. Moreover, it can be coupled to a thorium fuel cycle, which produces less long-lived radioactive waste and allows a sustainable energy production, as thorium is three times more abundant on Earth than uranium. However, before the Molten Salt Reactor technology can be realized, a thorough safety assessment of all components of the reactor must be carried out.
One main challenge for the development of the MSR technology and its commercialisation in the near future is a thorough understanding and assessment of the thermo-physical properties (e.g melting temperature, heat capacity, density, viscosity, thermal conductivity) of the molten fuel salt during reactor operation. During irradiation, numerous fission products are generated, which will affect the chemistry and properties of the fuel. Understanding and modelling the irradiated fuel chemistry and properties is a strong requirement for the safety assessment. As part of the recently granted European project EUDURANCE, we are looking for a PhD candidate that will work on this challenging task.
Deadline : Open until filled
(14) PhD Degree – Fully Funded
PhD position summary/title: PhD Radiochemistry
Radionuclide therapy (RNT) is a treatment for metastasized cancer in which radionuclides emitting radiation are used to kill tumour cells. Different radionuclide can be used among which 166Ho. This particular radionuclide has relatively short-life, which makes supply to hospitals challenging. In this project, we aim at developing a 166Dy/166Ho radionuclide generator which will supply hospitals with 166Ho on site and on demand. This project is part of large NWO (Dutch Research Council) funded consortium “UNRANU: UNderstanding the RAdiobiology of therapeutic medical radioNUclides” which aims to unravel the radiobiological effects of different radioisotopes. You will collaborate with many stake holders from academia, industry and society (e.g. Erasmus MC, Radboudumc, Delft University of Technology, NRG|PALLAS, AlfaRim Medical, HUB Organoids MILabs, Quirem Medical, Radboud Translational Medicine B.V., Siemens Healthineers, TerThera b.v., URENCO Nederland, Von Gahlen, VSL National Metrology Institute, RIVM National Institute for Public Health and the Environment, Wetenschapsknooppunt Radboud Universiteit).
In this project you will work on the development of 166Dy/166Ho generator by combining concepts from different fields including nanotechnology (synthesizing dysprosium-based nanomaterials) and radiochemistry (radiochemical separation to selectively extract Ho-166) to create the new generator and assess whether the produced Ho-166 conforms to the European pharmacopeia standards for use in the clinic. The research will be highly multidisciplinary, including all aspects of designing, synthesizing, characterizing, and fabricating the novel radionuclide generator in close collaboration with industry leaders and university hospitals.
Deadline : 15-06-2024
(15) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Systems and Systems Security
The Cybersecurity (CYS) group at the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) invites applications for full-time doctoral candidates in Systems and Systems Security. Successful candidates will develop novel methodologies and systems for increasing the security of systems software, that is, software written in low-level languages like C/C++ that operates in user or kernel space. Examples include developing techniques to harden software against exploitation of vulnerabilities and compartmentalization of software components. Successful candidates will have the opportunity to work closely with world-class researchers at TU Delft and our research collaborators in Europe and the US.
Deadline :June 13, 2024
(16) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Enhanced Image Processing for Scattered Light Microscopy
We offer an exciting PhD position at TU Delft on the junction of computational imaging, optics, image processing, and biomedical research. Apply now and contribute to a better reconstruction of complex fiber networks in the brain and other biological tissues.
The position is in the group of Dr. Miriam Menzel, at the Department of Imaging Physics. The group works on scattered light microscopy to visualize complex fiber networks. Your task will be to analyze measured scattering signals from different biological tissues, develop enhanced image processing tools to improve the reconstruction of underlying fiber structures, and inform the development of new measurement protocols. Possible projects include the automated classification of fiber types in histopathological tissues, or the estimation of structural sizes based on scattering measurements with different wavelengths.
Your main focus will be the development of software and analysis tools, but you will closely work together with the experimental team to further optimize the measurement procedure. If there is interest, you can test different experimental settings and perform tissue measurements yourself.
Computational Scattered Light Imaging (ComSLI) is a highly promising new imaging technique that resolves fiber pathways and their crossings with micrometer resolution. While other techniques require time-consuming raster-scanning of the sample, ComSLI yields scattering information for each image pixel in parallel and can be performed with a simple LED light source. The technique does not require any staining or labeling of the tissue, and reveals fiber organization in all kinds of biological samples (brain, muscle, tendon, bone), making it relevant for histopathology and clinical research.
Deadline : 12 June 2024
(17) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Integrated Planning and Learning for Drones
Drones have become increasingly prevalent in various domains including agriculture, environmental monitoring, search and rescue, and surveillance. To realise their full potential, new methods are needed to support autonomous decision-making within unstructured, dynamic environments.
Conventional approaches to drone mission planning often rely on predefined trajectories or reactive behaviours, limiting their versatility and robustness in real-world scenarios. In contrast, integrating planning with novel learning paradigms enables drones to dynamically adapt their actions based on environmental feedback and past experiences.
The PhD candidate will develop innovative planning algorithms which exploit elements of robotic learning. The research will especially focus on planning for small drones operating in challenging scenarios, e.g. monitoring plant parameters in greenhouses, where commonly used conventional methods fall short. The candidate will explore various techniques in robotic learning, ranging from reinforcement learning to active learning, with the aim of enhancing the autonomy, adaptability, and efficiency of embodied drone platforms in such scenarios.
The candidate will be guided by dr. Marija Popović at the Micro Air Vehicle laboratory (MAVLab), which is part of the Control and Operations department of the Faculty of Aerospace Engineering at TU Delft. They will have access to state-of-the-art tools and facilities needed to conduct their research. They will have the opportunity to collaborate with other researchers from within the laboratory, as well as on an international level.
Deadline :12 June 2024
(18) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Unraveling Cloud Patterns in the Climate System
We invite you to apply for a PhD position on utilizing concepts from theoretical physics in the field of atmospheric cloud research.
Clouds are the biggest obstacle to advances in our understanding of climate and climate change. Cloud fields organize into marvelous patterns that cover hundreds of kilometers over the subtropical and tropical oceans and have recently emerged as a key uncertainty for climate projections. While conspicuous in satellite imagery, we lack the concepts and tools to adequately model their evolution. The big challenge lies in capturing the multiple scales that govern cloud patterns – from the formation of cloud droplets on aerosol particles to shifts of prevailing cloud regimes in response to changing weather statistics.
During your PhD research, you will explore concepts from complex systems theory and data-driven approaches to multiscale systems to unravel the mesoscale cloud patterns that keep us from better climate projections. You will apply these approaches to large data sets of cloud patterns. You will work with existing data from simulations and satellites as well as perform your own numerical simulations. Within this scope, we will guide you in designing specific research questions that take your own ideas and interests into account.
Your PhD project will be supervised by Franziska Glassmeier. Together with two PhD students, two postdoctoral researchers, and an advisory AI team, we will tackle the challenges of the ERC Starting Grant project MesoClou. You will also be part of our broader Atmospheric Science research group, which has a strong focus on modeling and observing clouds. In addition to an exciting research project and a friendly, supportive and stimulating work environment, we can offer you generous funds for scientific travel, including extended research visits abroad.
Deadline :10 June 2024
(19) PhD Degree – Fully Funded
PhD position summary/title: PhD Position in AI-Supported Learning Ecosystem
TU Delft is a top-tier university and we have been growing in our investment in the field of Artificial intelligence. Within the University, the Management in the Built Environment (MBE) Department strives for a sustainable built environment in which the interests of the end user and other stakeholders are the starting point. MBE focuses on solutions for developing and managing buildings, portfolios and urban areas and training the next generation of managers in the built environment. MBE is part of the Faculty of Architecture and the Built Environment. The PhD will be hired by MBE, but positioned in the Institute for Advanced Metropolitan Solutions in Amsterdam, of which TU Delft is founding partner. The PhD candidate will collaborate with the PhD candidates and other researchers involved in the Future-Proof Living Environment project. The AMS Institute offers a thriving transdisciplinary community in the city centre of Amsterdam. The PhD candidate will be enrolled in the Graduate School of the Faculty of Architecture and the Built Environment and is therefore also expected to be regularly in Delft for courses, meetings and events.
We are looking for a PhD candidate who is going to be involved in a programme “Future-Proof Living Environment: Transition towards zero-emission, circular and climate proof buildings and infrastructures”, funded by the National Growth Fund in the Netherlands. The PhD position is part of the sub programme “Living Lab Learning Environments”. Aim is to develop a scientifically grounded co-creative approach to innovation which is reproduceable and scalable. It aims to do so by combining testing and implementation of new technologies with social innovation in living labs, in which social innovation is about enabling flexible and reproducible way of learning. Within this focus, this PhD position will hone in on the role of data and digital innovation, including Artificial Intelligence, in planning, design and decision-making for a future-proof living environment. In the context of the built environment, there has been an increasing focus on digitalisation and digital tools recently to understand the transformative changes in cities. However, understanding the complexities and interdependencies between different scales and levels is difficult to predict and conceptualise in practice. In that sense it is important to understand how digitalization/Artificial Intelligence/data can help to make the choices between priorities within cities and within neighbourhoods, bringing also the multi-scalar interdependencies of choices into focus. In this PhD, we aim to use urban experiments/living labs to gain understanding of how an experimental approach on multiple spatial levels can help to make a multi-level transition in the built environment. In other words, this PhD position explores how digitalization/digital tools can be supportive in deciding on what transformative changes to make in the built environment..
Deadline : Open until filled
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(20) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Numerical Methods for Stochastic Differential Equations
The Analysis research group within the Delft Institute of Applied Mathematics at TU Delft is offering a full-time PhD position in the area of Numerical Methods for Stochastic Differential Equations. The PhD candidate will be supervised by Dr. Kristin Kirchner (TU Delft and KTH Royal Institute of Technology Stockholm).
For more information regarding Kristin Kirchner’s research, please visit her webpage.
The focus of the project lies on the numerical approximation of solutions to stochastic differential equations, which due to the form of these equations are not Markov processes. Such stochastic processes “with memory” are of importance in several disciplines, such as for financial and statistical modelling. However, due to the correlation structure their computational simulation is very challenging. The PhD project addresses this challenge. Possible topics are computational methods for stochastic equations driven by fractional Brownian motion and multilevel Monte Carlo methods. Applications include option pricing under rough volatility models in finance and long-range dependencies in statistics.
The project includes the budget for a research visit at KTH Royal Institute of Technology in Stockholm, Sweden.
Besides carrying out mathematical research and working on scientific articles, the PhD candidate is expected to actively participate in seminars and discussions with colleagues from the department as well as to present the findings at (inter)national conferences or workshops. Furthermore, the candidate is supposed to carry out minor teaching tasks (at most 15% of the contract time). Teaching in English will be possible. Communication will be in English.
Deadline : June 7, 2024
(21) PhD Degree – Fully Funded
PhD position summary/title: Two (2) PhD Positions on Algorithms for Formal AI Verification and Explainability
You will conduct both theoretical and empirical research at the intersection of logic, optimization, machine learning, control, monitoring, interpretability, and visualization. Both PhD projects are inspired by real-world deployment of AI, with one leaning towards advancing theory and the other to be done in close collaboration with domain experts from the Netherlands Railways (NS), leading to significant scientific as well as practical impact.
You will be part of the Algorithmics Group in the Department of Software Technology of the Faculty of Electrical Engineering, Mathematics and Computer Science. You will work in a dynamic and diverse environment of other PhD and postdoc researchers excited about making theoretical and algorithmic contributions in intelligent decision making.
Deadline : June 7, 2024
(22) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Modeling and Monitoring of Fiber-reinforced Composite Ships Under Extreme Loads
Fiber-reinforced composite (FRC) structural members offer significant potential for ships, including reduced weight, corrosion resistance, enhanced strength-to-weight ratio, optimized performance, noise and vibration reduction. These can lead to improved operational effectiveness, maintenance schemes, and crew comfort. Next to these advantages, utilization of composite materials for fast ocean-going vessels also requires addressing a number of outstanding challenges, e.g. operation under extreme loading and the effect on structural integrity. Slamming loads or greenwater loads, together referred to as (wave) impact loads, can induce heavily-stressed locations in the composite member through (often invisible) delamination, fiber breakage, matrix cracking, etc. leading to a reduced fatigue lifetime of the structure.
This project deals with an in-depth investigation of slamming loads on FRC hulls and superstructures. The study will focus on improved analysis and understanding of slamming loads, possibilities for monitoring these loads using on-board and/or embedded sensor systems, data-driven estimation of stress state in heavily-loaded areas due to slamming loads, and assessment of micro- and macro-damage incurred to the structure under such
loading conditions that can affect the lifetime of the vessel. Collaboration with highly-recognized (industrial) partners and other PhD researchers involved in the program is envisaged.
Deadline : 6 June 2024
(23) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Dynamic Modeling of Criminal Power Structures
The complexity, covert nature and ever-changing power structures of organized crime pose enormous challenges to police strategies, tactics and operations. To effectively combat organized crime and criminal exploitation, a robust approach is crucial, which enables the police to continuously adapt to the changing reality. As a PhD at TU Delft you will have the unparalleled opportunity to conduct unique research and stimulate the application of the latest technology within the police.
In this 5-year project you will spend 20% of your time with the Police. You will work closely with police officers and scientists and thus benefit from their in-depth knowledge, insights and expertise. Your research is based on practical cases relating to, for example, narcotics, money laundering and corruption. By using system dynamics to capture complexity and deep uncertainty, you model basic structures of crime organizations. Your models use limited available data, yet generate a wide range of possible outcomes and robust interventions. You also focus on sharing knowledge with scientific and non-scientific stakeholders in the police. This enables police scientists to continuously adapt their models to new realities. And you help policymakers, police officers and investigators to use the insights obtained effectively.
Your project is part of the Model-Driven Decisions (MoDD) Lab, a Police -TU Delft initiative. In the MoDD Lab you join an interdisciplinary community of four fellow PhD students. Together you share knowledge to address model-based decision making from different perspectives. Of course you also write scientific articles and speak at leading conferences. At TU Delft you will join the driven and internationally diverse Policy Analysis section of the Multi-Actor Systems department. Our team employs around 60 academic staff, PhD students and postdocs in areas ranging from health and safety to infrastructure and climate. We share the drive to improve policymaking and foster a relaxed, collegial and collaborative atmosphere. And we give you all the support you need to grow your career as a researcher.
Deadline : Open until filled
(24) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Automated activity detection from multiple sensors
In the security domain, one of the biggest challenges is analyzing and interpreting simultaneous data streams from multiple sensors. Manually processing this data is intensive, time-consuming and error-prone. To increase efficiency, computer vision can play a crucial role in video data processing. Until now, the focus has been on analyzing data from a single camera. As a PhD at TU Delft you will conduct unique research and design machine learning algorithms for detecting relevant activities from multiple cameras, and possibly other sensors as well. Do you want to work closely with the Police and help increase efficiency in the security domain?
In this five-year project you will study literature on computer vision methods for multiple cameras, possibly extended to combinations with other sensors, such as radar or microphones. In addition, you will develop and test machine learning algorithms that can track people and objects, such as cars, using input from cameras and sensors in different locations and at different times. The end goal is ML-driven detection of complex activities, by designing neural networks for evolving spatio-temporal graphs to integrate information from multiple sources on-the-fly. As your research progresses, you will help the police force implement and use your algorithms, for which you will also coach and train police users.
You will share your findings and knowledge, speak at conferences and write scientific articles. Your project is part of the Model-Driven Decisions Lab, a Police-TU Delft initiative, where you are part of an interdisciplinary community of four fellow PhD students. Together you will share knowledge to address model-based decision making from different perspectives. To cultivate collaboration with stakeholders and anchor solutions in their processes, you spend 20% of your time at the Police. At TU Delft you will join the driven and internationally diverse team of academic staff and PhD students of the Signal Processing Systems section. We foster a hospitable, collegial and cooperative atmosphere, in which we share knowledge and also spend social time. We give you all the support and training you need to develop both personally and professionally.
Deadline : Open until filled
(25) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Influence of Demographic and Social Developments on Police Operations
Cuts in mental health care, addiction care and youth care, an aging population and increasing frustration about social inequality are just a few developments that have a major impact on the functioning of the police. In addition, the persistent shortage on the labor market and the aging population also influence the capacity of the police. For robust policing strategies and effective policymaking, a thorough understanding of this complex and ever-changing reality is crucial. As a PhD candidate at TU Delft, you develop dynamic system models to map these developments and uncertainties. Do you want to see your research applied in the real security domain?
You conduct unique research into the impact of various phenomena on police operations, capacity and competencies. Based on realistic use cases, you investigate the impact of demographic and social developments. And you investigate and model these developments, how they interact and add spatial dimensions in system dynamic models. In this 5-year project you will spend 20% of your time with the Police. By working closely with police experts, you benefit from their experience, expertise, in-depth insights and available data and models. To stimulate the adoption of your models, you develop simulation models under uncertainty aimed at knowledge development and retention in the complex, multidisciplinary police organization. Of course you also write scientific articles and speak at leading conferences.
Deadline : Open until filled
(26) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Optimal exploration of networks
In crime fighting, it is crucial to be able to predict how actors will respond to actions. Whether the police act on the basis of captured information, or intervene in, for example, a money laundering or drug chain, every action will provoke an uncertain response. As a PhD at TU Delft you bridge the gap between fundamental research and police practice. To improve the predictive models used by the police, you use reinforcement learning to optimize the exploration of networks. Do you want to see your research applied in the real security domain?
The police use several models to increase their effectiveness. However, the data, represented as interconnected networks of nodes, is limited, while the potential response to disruptions is variable. You will develop algorithms to identify and prioritize the uncertainties in networks, assess the risks of interventions and predict the response of actors to disruptions. And you’ll use simulations to test your improved models. Of course you write scientific articles and speak at leading AI conferences. In this 5-year project you will spend 20% of your time in the police. By working closely with police experts, you benefit from their experience, expertise and in-depth insights.
Your project is part of the Model-Driven Decisions (MoDD) Lab, a Police-TU Delft initiative. In the MoDD Lab you join an interdisciplinary community of four fellow PhD students. Together you share knowledge to address model-based decision making from different perspectives. At TU Delft you will join the driven and internationally diverse team of around 20 academic employees, PhD students and postdocs of the Sequential Decision Making section. We share the ambition to be the world’s top scientists in the field of AI and machine learning, and encourage you to spar with us. In a welcoming and cooperative atmosphere, we will give you all the support you need to grow your career as a researcher.
Deadline : Open until filled
(27) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Automated Activity Detection from Multiple Sensors
In the security domain, one of the greatest challenges is analysing and interpreting a torrent of data, including the videos produced by multiple surveillance cameras. Suffice to say that the manual process is very time-consuming and error-prone. To help drive efficiency, computer vision is crucial, yet so far the focus has been on single-camera data analysis. As a PhD at TU Delft you will conduct unique research and design machine learning algorithms for detecting relevant activities from multiple cameras, and possibly other sensors as well. Would you like to work closely with the Netherlands Police and help drive efficiency in the security domain?
In this five-year project, you will review literature on computer vision methods for multiple cameras, possibly extended to combinations with other sensors, such as radar or microphones. In addition, you will develop and test machine learning algorithms that can track people and objects, such as cars, using the input from cameras and sensors at different locations and at different times. The end-goal is ML-driven detection of complex activities, by designing neural networks for evolving spatio-temporal graphs to integrate multi-source on-the-fly information. As your research progresses, you will assist the police force with implementing and using your algorithms, for which you will also coach and train police users.
As part of your role, you will disseminate your findings and knowledge, speaking at conferences and writing scientific articles. Your project is part of the Model-Driven Decisions Lab, a Netherlands Police-TU Delft initiative, where you will join an interdisciplinary community of four fellow PhD students. Together, you will share knowledge to tackle model-based decision-making from different perspectives. To cultivate collaboration with the stakeholders and embed solutions in their processes, you will spend 20% of your time at the Netherlands Police. At TU Delft, you will join the driven and internationally diverse team of academic staff and PhD students of the Signal Processing Systems section. We foster a welcoming, collegial and collaborative atmosphere, in which we share knowledge and spend time socially too. We will give you all the support and training you need to evolve both personally and professionally.
Deadline : June 2, 2024
(28) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Dynamic Modelling of Criminal Power Structures
The complexity, covert nature and constantly changing power structures of organised crime pose a huge challenge to police strategies, tactics and operations. To effectively combat organised crime and criminal exploitation, a robust approach that enables the police to continuously adapt to changing realities is crucial. As a PhD student at TU Delft, you will have the unparallelled opportunity to conduct unique research and drive adoption of the latest technology within the Police.
In this 5-year project, you will spend 20% of your time at the Police. You will be working closely with police officers and scientists, thus benefiting from their in-depth knowledge, insights and expertise. Your research will be based on real-world cases related to e.g. narcotics, money laundering and corruption. Harnessing system dynamics to capture the complexity and deep uncertainty, you will model basic structures of crime organisations. Your models will leverage limited available data, yet generate a broad scope of potential futures and robust interventions. You will also focus on sharing knowledge to scientific and non-scientific police stakeholders. That’s how you will enable police scientists to continuously adapt your models to new realities. And you will help decision-makers, officers and detectives effectively use the insights gained.
Your project is part of the Model-Driven Decisions (MoDD) Lab, a Police -TU Delft initiative. At the MoDD Lab, you will join an interdisciplinary community of four fellow PhD students. Together, you will share knowledge to tackle model-based decision-making from different perspectives. You will, of course, also write scientific articles and speak at leading conferences. At TU Delft you will join the driven and internationally diverse Policy Analysis section of the Multi-Actor Systems department. In our team, some 60 academic staff, PhD students and postdocs work in fields ranging from health and security to infrastructure and climate. We share a drive to improve policy-making and foster a relaxed, collegial and collaborative atmosphere. And we will give you all the support you need to grow your career as a researcher.
Deadline : 2 June 2024
(29) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Impact of Demographic and Societal Developments on Police Operations
Cutbacks in mental health, addiction and youth care, an ageing population and growing frustration over social inequality are just some of the developments that have a resounding impact on police operations. In addition, the ongoing tight labour market and ageing also affect the police force’s capacity. To secure robust police strategies and effective policy-making, a thorough understanding of this complex and continuously changing reality is crucial. As a PhD candidate at TU Delft, you will develop dynamic systems models to capture these developments and uncertainties. Would you like to see your research applied in the real-world security domain?
You will conduct unique research into the impact of multiple phenomena on police operations, capacity, and competencies. Based on real-world use cases, you will look into the impact of demographic and societal developments. And you will research and model those developments, how they interact, and add spatial dimensions in system dynamics models. In this 5-year project, you will spend 20% of your time at the Police. Working closely and interacting with police-experts, you will benefit from their experience, expertise, in-depth insights and available data and models. To stimulate adoption of your models, you will develop simulation models under uncertainty geared to knowledge development and retention in the complex, multidisciplinary police organisation. You will, of course, also be writing scientific articles and speaking at leading conferences.
Your project is part of the Model-Driven Decisions (MoDD) Lab, a Police – TU Delft initiative. At the MoDD Lab, you will join an interdisciplinary community of four fellow PhD students. Together, you will share knowledge to tackle model-based decision-making from different perspectives. At TU Delft, your home base will be the Policy Analysis section of the Multi-Actor Systems department. You will join our driven, internationally diverse team of some 60 academic staff, PhD students and postdocs. Working in fields ranging from health and security to infrastructure and climate, we share a drive to improve policy-making. We foster a relaxed, collegial and collaborative atmosphere. And we will give you all the support you need to grow your career as a researcher.
Deadline : 2 June 2024
(30) PhD Degree – Fully Funded
PhD position summary/title: PhD Position in Activating History and Heritage of Amsterdam’s Bridges and Quaywalls for Sustainable Development and Protection
The UNESCO Chair Water, Ports and Historic Cities of the PortCityFutures center, located in the Chair History of Architecture and Urban Planning at TU Delft explores the relation between water and historic cities through the lens of the World Heritage site of Amsterdam. The Chair History of Architecture and Urban Planning at TU Delft is partner in the Groeifonds project Multifunctional Urban Waterfronts. The PhD researcher will collaborate with members of the Chair to develop and refine methodologies that help us link historical analysis and long-term thinking on quaywalls and bridges in the context of the World Heritage property Amsterdam, the historic urban ensemble of the canal district built at the turn of the 16th and 17th century, to future solutions that protect heritage and allow for sustainable development in line with the UNESCO Historic Urban Landscape approach. The PhD researcher will be employed to co-lead Work package 7 and to connect heritage approaches to research by design.
Deadline : Open until filled
(31) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Optimal Exploration of Networks
When fighting crime, it is crucial to be able to forecast how actors are going to respond to actions. Whether the police acts on information captured, or intervenes in e.g. a money laundering or drugs chain, any action will provoke an uncertain reaction. As a PhD candidate at TU Delft, you will bridge fundamental research and police practice. To enhance the predictive models used by the police, you will harness reinforcement learning to optimise the exploration of networks. Would you like to see your research applied in the real-world security domain?
The police use multiple models to enhance their effectiveness. Yet the data, represented as interconnected networks of nodes, are limited, while the potential response to disruptions is volatile. You will develop algorithms to identify and prioritise the uncertainties in networks, assess the risks of interventions, and predict the actors’ response to disruptions. And you will use simulations to test your enhanced models. You will, of course, be writing scientific articles and speaking at leading AI conferences. In this 5-year project you will spend 20% of your time at the Netherlands police force. Working closely with police experts, you will benefit from their experience, expertise and in-depth insights.
Your project is part of the Model-Driven Decisions (MoDD) Lab, a Police-TU Delft initiative. At the MoDD Lab, you will join an interdisciplinary community of four fellow PhD students. Together, you will share knowledge to tackle model-based decision-making from different perspectives. At TU Delft, you will join the driven and internationally diverse team of some 20 academic staff, PhD students and postdocs of the Sequential Decision Making section. We share the ambition to be the world’s top scientists in the field of AI and machine learning, and encourage you to spar with us. Fostering a welcoming and collaborative atmosphere, we will give you all the support you need to grow your career as a researcher.
Deadline : Open until filled
(32) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Quantitative MRI
The Magnetic Resonance Systems lab (Mars lab) at TU Delft works relentlessly on the advancement of quantitative Magnetic Resonance Imaging. We are presently recruiting a PhD student to pursue their four year doctorate in our lab funded by a prestigious ERC Starting Grant (VascularID). The successful candidate will join a young and dynamic group in a prolific, creative and fun environment.
The starting date for this position is August 1st, 2024 or sooner. The PhD student will be leading their own research projects integrated into an innovative research line that aims to uncover new MRI signal phenomena in the microvasculature and develop novel acquisition schemes to use those as biomarkers with quantitative MRI. This project will comprise a strong biophysical modeling and MRI sequence development part, as well as experiments in inanimate phantom objects and applications in vivo. The successful candidate will also be involved in other ongoing research projects at the lab including
reconstruction projects. At advanced stages, the Ph.D. student will have the opportunity to take mentoring responsibility for students at the bachelor’s and master’s levels. Please get in touch to find out more details about the content of the project.
The primary infrastructure for MRI research at the Mars lab is a new Philips Ingenia 3T scanner, located in a local clinical research center with substantial scan time dedicated to our lab for research purposes. Select studies will also be performed on Siemens Prisma 3T scanners of national collaboration partners.
The position is in the Department of Imaging Physics (www.imphys.tudelft.nl) of the Faculty of Applied Sciences. This department performs cuttingedge research spanning the range from understanding the basic principles underlying imaging technology to
automated image analysis.
Deadline :2 June 2024
(33) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Value-based assessment methods for AI systems
Validating models is a crucial step before decisions can be made about implementation and is important for continuous monitoring of systems in use. The challenge is, however, that validation needs to happen along a range of different values that are important for AI to possess at the police: accuracy, but also fairness, reliability, trustworthiness, and more need to be ensured.
As a PhD student at TU Delft, you will conduct impactful research on two key aspects in advancing the responsible use of AI within the police force.
First, you will investigate the standards and values surrounding AI usage, particularly concerning open models. This entails defining what criteria these models must meet, beyond common considerations like bias and fairness. You will delve into various aspects beyond these to ensure a comprehensive framework. Second, you will also design methods to systematically evaluate a range of models against these established standards and values. This contribution ensures the responsible deployment of AI within the police force and maximizes the efficiency of utilizing publicly available models.
Through these efforts, you will significantly contribute to fostering an ethical and accountable AI culture within the organization.
Deadline : June 2, 2024
(34) PhD Degree – Fully Funded
PhD position summary/title: PhD Positions Advanced Space Propulsion Systems
For a recently granted EU project, TU Delft will collaborate with other high-level European universities and industrial partners for the development of a bimodal propulsion system (main orbital control propulsion + reaction control thrusters) based on shared green propellants, produced in orbit through an innovative conversion process applied to liquid water.
More specifically, the areas under the direct responsibility of TU Delft will be: the development and testing of a small solar thermal thruster (1 N class) for the reaction control part of the system; the development and testing of an innovative concept of tank for the storage of low-density gaseous propellants; the definition of concrete mission scenarios in which the bimodal system could be used, and their relevant requirements.
Two PhD positions are available at TU Delft to work on this project under the supervision of Dr. Angelo Cervone, with mandatory (non-negotiable) start date in October 2024.
Deadline :02 June 2024,
(35) PhD Degree – Fully Funded
PhD position summary/title: PhD position DoAnt: Joint Development of Sparse Antenna Array Topology and High Resolution DoA Estimation
Constantly evolving advanced driver assistance systems (ADAS) demand radar sensors with high angular resolution for object detection and classification. Improvement of angular resolution is conventionally achieved by increasing the number of transmit and receive antennas, which lead to higher cost, processing and thermal requirements. This has motivated the use of sparse multiple-input-multiple-output (MIMO) array topologies in automotive radars. However, the current topology synthesis methods rely on a quasi-random layout selection after an exhaustive search, which becomes suboptimal, and even infeasible for a large number of antennas. Besides, the electromagnetic (EM) effects, such as mutual coupling, are ignored in the system development phase, which causes performance degradation (degree of arrival (DoA) errors, high interference) and/or heavy calibration need.
To achieve high resolution and overall robust performance of angular estimation with sparse arrays, joint development of MIMO array topologies (sampling problem) and angular estimation algorithms (estimation problem) is required, while considering the physical effects (EM problem). The novel idea in DoAnt is to develop an EM-driven optimization framework for sparse radar sensor arrays integrated with DoA estimation. With its interdisciplinary nature, DoAnt aims to synthesize innovative sparse array topologies for automotive radar antennas at 77 GHz to improve performance, reliability and robustness of DoA estimation against EM non-idealities.
Deadline :May 31, 2024
(36) PhD Degree – Fully Funded
PhD position summary/title: PhD Position EUROGUARD: European Goal Based Multi Mission Autonomous Naval Reference Platform Development
Recent geo-political developments and related security challenges have accentuated the need for rapid response capabilities by well-coordinated EU naval vessel fleets with advanced platform and systems’ technologies. On the requirements side, rapid adaptability to ever changing scenarios, optimal capabilities deployment and resilience along with high-level situational awareness and minimum human-on-the spot presence are needed. On the solutions side, high vessel automation along with safe communications and effective combat management must be provided. In addition, EU naval fleets are committed to reduce their environmental footprint by intelligent use of Power and Propulsion Energy (PPE) systems and clean fuels.
The relations between the naval vessel functions and the corresponding physical vessel systems must be referenced and implemented in the vessel architecture so that the impact of design choices and new technologies can be made visible. This also means that differing naval missions and environmental conditions require different vessel solutions and Concepts of Operations (CONOPS). The ability to capture the European operational concepts, the required functionalities, and specific constraints per intended CONOPS can be provided by a common System Architecture for European navies.
Project EUROGUARD will combine state-of-the-art and innovative technologies to study, design, prototype and demonstrate the viability of a versatile medium-sized semi-autonomous surface (MSAS) naval vessel. In addition, the MSAS will also demonstrate the benefits offered by a common System Architecture for future European semi- and fully autonomous naval ships.
Deadline : 31 May 2024
(37) PhD Degree – Fully Funded
PhD position summary/title: PhD Position in Soft Matter: Flow and Dynamics of Colloidal Gels
A PhD position is currently available in the Garbin lab (https://garbinlab.org) on novel methods to control the microstructure of colloidal gels. You will be part of the CoCoGel Marie Skłodowska-Curie Industrial Doctoral Network (https://cocogel.iesl.forth.gr), together with 14 other PhD candidates, who will be working throughout Europe on bringing rational design to the structuring of colloidal gels.
Colloidal gels – soft materials made of attractive colloidal particles – form the basis of many products and materials, including personal care products, battery electrodes, and construction materials. The development of new methods to tune the properties of colloidal gels will enable the next generation of sustainable products and processes. We have recenlty discovered a novel, ultrasound-induced mechanism that creates local order in colloidal gels [Saint-Michel, Petekidis & Garbin, Soft Matter 18, 2092 (2022)]. In this PhD project, you will investigate the physical phenomena underlying this new mechanism and explore its potential for controlled structuring of model colloidal gels and new products. You will perform experiments to elucidate the effect of process parameters on the resulting microstructure using confocal microscopy and microstructural analysis.
As PhD candidate in the CoCoGel network, you will participate in collaborative and training activities hosted by the network partners. You will be based at Delft University of Technology and, as part of your PhD, you will be periodically seconded at industrial partners of CoCoGel to develop elements of your project. A secondment of up to 8 months will be hosted by InProcess LSP, a scale-up company that develops process analytical technology for in-line characterization of dense suspensions using optical coherence tomography (OCT). Here, you will explore the possibility to overcome the limited time resolution of confocal microscopy using OCT. To explore applications of your scientific findings to personal care and food products, you will be seconded at Unliever for up to 6 months. To explore applications in battery electrodes, you will be seconded at Systems Sunlight SA, manufacturer of batteries and storage systems for energy produced by renewable sources.
Deadline : Open until filled
(38) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Optimal Train Trajectory Coordination Under Uncertainty
The ever-increasing demand for travel requires transport systems to offer more capacity. One approach is to maximize the utilization of existing infrastructure to run vehicles as closely as possible, thereby enabling the operation of more vehicles. However, this presents challenges in accurately defining the varying minimum safe distances between vehicles at different locations and optimally coordinating the trajectories of multiple vehicles over the infrastructure to ensure conflict-free operations. The uncertainty in operations, such as unexpected traffic disturbances, further complicates the task of designing a trajectory coordination plan that can maintain feasibility under varying conditions.
In this research, you will focus on the optimal trajectory coordination of railway vehicles, revolutionizing railway systems through high-performance operations, reducing time intervals from minutes to seconds. You will develop mathematical models to optimally coordinate train trajectories at critical points in dense railway networks. In particular, you will analyse and characterize different types of critical points, and develop multi-train trajectory optimization models and solution methods, considering uncertainty in train operations. You will work closely with Netherlands Railways (NS), who will provide real-world data, including realized train trajectories from past operations. You will apply the developed methodology to case studies from the Netherlands Railways and provide guidelines to improve train timetabling.
The PhD research falls within the topic of railway transport planning and operations, and builds on state-of-the-art research on train timetabling and energy-efficient train operation. You will typically be using methods from operations research, optimal control, stochastic optimization and reinforcement learning.
Deadline : 31 May 2024
(39) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Tectonic Geomorphology: Evolution of Northern Africa for the Next 1000 Years
This PhD project, hosted by the Geoscience and Engineering Department of the TUDelft, is a unique opportunity to shape our understanding of geological evolution and the impact it will have on future landscapes and water resources in northern Africa (in this project: above the equator). Positioned at the intersection of tectonic geomorphology and hydrogeology, this project is not just about forecasting; it’s about forming practical findings for mitigating the challenges of demographic growth and climate change. Through a multidisciplinary approach that makes use of landscape evolution modelling (BADLANDS), you will lay the groundwork for pivotal policy-making and sustainable resource management strategies that may have impact in the centuries to come. This project links to the UN Sustainable Development Goal #6 ‘clean water and sanitation’ as well as forecasting impacts on one of the Planetary Boundaries, namely ‘land-system change’.
Deadline : May 31st 2024
(40) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Understanding the Ecophysiological Controls of Biological N2O Emissions
Nitrous oxide (N2O) is a potent greenhouse gas almost 300 times stronger than CO2. Globally, the majority of N2O originates from biological conversions in natural and engineered ecosystems. Despite decades of research, the environmental conditions controlling the underlying microbiology remain largely unknown.
This PhD project aims to advance our fundamental understanding of biological N2O formation as a basis for robust emission mitigation strategies. You will run parallel bio-reactors to enrich for nitrogen transforming communities, and understand how they assemble and function under highly-controlled non-axenic conditions. Cutting-edge meta-omics approaches, including metagenomics and metaproteomics, will be employed to resolve the function of each community member and their metabolic interactions in dynamic environments. Special emphasis will be given to as-of-yet overlooked processes and metabolic nitrogen intermediates.
The project bridges the Sanitary Engineering and Environmental Biotechnology groups at TU Delft. You will be directly supervised by Michele Laureni, Jules van Lier and Mark van Loosdrecht, with ample freedom to explore your own curiosity.
Deadline : 31 May 2024
(41) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Utilizing Runtime Information to Improve Development Processes
Context: Runtime information such as metrics, traces, and logs is currently outside the scope of the IDE. It is possible to develop, design, and refactor a system in an IDE, but only a few plugins make it possible to consider the available runtime information during the development process in the IDE. Developers can use various observability tools to receive alerts and create dashboards that can impact development, but getting feedback from runtime information directly in the IDE is difficult.
Goal: The goal of this project is to study how we can process runtime and observability data to improve the development process in the IDE. There are a lot of different research directions, such as anomaly detection, improving code generation and refactoring, creating smart tips for developers, and developing debugging and profiling assistants. The solution can be either ML or non-ML.
Note: This PhD position is part of the AI for Software Engineering lab (AI4SE), a collaboration between JetBrains and Delft University of Technology. Hence, the prospective PhD student will also work closely with researchers and developers of JetBrains. More information is available at https://lp.jetbrains.com/research/ai-for-se/. (edited)
Deadline :31 May 2024
(42) PhD Degree – Fully Funded
PhD position summary/title: PhD Position AI/ML Algorithms for 6G Telemetry Systems
Join the frontier of innovation in 6G: the future of mobile networks technology! In the Netherlands, a unique alliance of 60 top-notch ICT companies, semiconductor firms, and research institutions has united to spearhead specific aspects of 6G: (1) software antennas, (2) AI-driven network software, and (3) groundbreaking 6G applications. Join us as a PhD student in this prestigious Future Network Services (FNS) flagship project, where research and entrepreneurial pursuits converge.
6G aspires to connect seamlessly new types of user devices ranging from IoT nodes and wearables to drones and autonomous vehicles; to leverage new types of technologies/interfaces; and to fully-integrate in its fabric vertical / application-level mechanisms. This unprecedentedly complex ecosystem calls for a clean-slate approach in the design of network monitoring and telemetry analysis solutions. These tools need to be able to run at real-time, enable extremely accurate inferences, and at the same time reduce data collection / processing costs and overheads. There is wide consensus that the role of network telemetry will be pivotal in optimizing the performance of 6G, yet it remains unclear how to achieve this promise.
Your PhD pursuit aims to pioneer AI/ML algorithms for tackling this problem heads on through the design of zero-touch monitoring, telemetry analysis and controlling of 6G communication systems. You will leverage state-of-the-art AI/ML techniques such as Reinforcement Learning, Deep Learning, Bayesian Learning and Federated/Split Learning to design 6G telemetry mechanisms with extremely high inference accuracy and minimal overheads; and further explore the potential of emerging AI paradigms, such as Generative AI and Neurosymbolic AI for the design of the next generation of telemetry solutions. These AI/ML tools will be tested in practice, using TU Delft’s cutting-edge programmable network
Deadline : Open until filled
(43) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Cybersecurity of 6G Networks
Join the frontier of innovation in 6G: the future of mobile networks technology! In the Netherlands, a unique alliance of 60 top-notch ICT companies, semiconductor firms, and research institutions has united to spearhead specific aspects of 6G: (1) software antennas, (2) AI-driven network software, and (3) groundbreaking 6G applications. Join us as a PhD student in this prestigious Future Network Services (FNS) flagship project, where research and entrepreneurial pursuits converge.
In contrast to previous mobile network generations, 6G will evolve towards open and virtualized RANs (Radio Access Networks). While this evolution will bring many benefits, like breaking vendor lock-in, it may also introduce new security challenges. Attacks could target the open/virtualized RAN and subsequently permeate to the core network. Novel security mechanisms are therefore imperative for the successful deployment of 6G. Your PhD research seeks to pioneer intelligent security analytics for a zero-trust 6G infrastructure. This includes developing machine-learning models adept at detecting and neutralising threats and cyber-attacks within 6G networks. Your work will advance the state-of-the-art by designing architectural solutions that fortify the security of 6G systems. Additionally, your research will shed light on novel attacks that could target these systems, evaluating their (potential) impact and severity.
Your home base will be the Cybersecurity group, where you will be supervised by Prof. Georgios Smaragdakis. You will also receive guidance, on the topic of 6G, from Prof. Fernando Kuipers and the Networked Systems group. Hence, you will have ample support and opportunities for sparring and knowledge sharing. Together, we will secure the future of mobile communications!
Deadline : Open until filled
(44) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Embedded AI for 6G Networks
Join the frontier of innovation in 6G: the future of mobile networks technology! In the Netherlands, a unique alliance of 60 top-notch ICT companies, semiconductor firms, and research institutions has united to spearhead specific aspects of 6G: (1) software antennas, (2) AI-driven network software, and (3) groundbreaking 6G applications. Join us as a PhD student in this prestigious Future Network Services (FNS) flagship project, where research and entrepreneurial pursuits converge.
6G networks will utilise frequencies from sub 6 GHz to mmWave and THz bands, thereby substantially increasing the number as well as the heterogeneity of the required base stations. This complexity is further exacerbated by (1) the emergence of new network architectures like cell-free networks, (2) novel network services, such as joint communication and sensing, and (3) the evolution to space-air-ground networks. Amidst this complexity, edge AI emerges as a promising solution directly integrated into hardware devices at the edge. Your PhD research revolves around optimising these AI algorithms for base stations with different hardware and computing capabilities. Work on advanced edge/embedded AI models tailored for managing the densely packed frequency bands in 6G networks, and enabling seamless connectivity across space, air, and ground base stations. Your work takes a novel approach by co-designing these models alongside the hosting hardware, ensuring optimal performance and scalability. The result? Hardware-aware, adaptable AI models attuned to the dynamic nature of 6G networks, adjusting model complexity based on available computing resources.
Your home base will be the Embedded Systems group, where you will be supervised by Dr. Qing Wang. You will also receive guidance from Prof. Fernando Kuipers and the Networked Systems group. Hence, you will have ample support and opportunities for riveting sparring and knowledge sharing.
Deadline : Open until filled
(45) PhD Degree – Fully Funded
PhD position summary/title:
The 3D Geoinformation Group and AiDAPT Lab invite applications for a funded PhD position in the area of Data-/AI-driven infrastructure inspection and maintenance planning. We are looking for candidates highly motivated to work at the confluence of AI, geoinformation, infrastructure safety & sustainability, and algorithmic decision-making, in order to address the life-cycle extension needs of our aging and growing transportation systems.
A significant part of our transportation infrastructure, including roads and bridges, has reached or exceeded its design life. To extend the life of infrastructure in the future and at the same time meet sustainability goals, it is necessary to optimize interventions by predicting the most effective inspection and maintenance strategies, while reducing emissions, and network disruptions due to construction works or poor asset structural conditions, among others.
This PhD research will investigate how optimal inspection and maintenance strategies can be devised using geo-data, engineering models, and AI. The case study will be the city of Amsterdam, however, the methods to be developed will be generic. The PhD research will commence with an inventory of the already existing inspection and maintenance data and will first develop a method to get structured insight into degradation over time based on these historical geo-based data. In a second step, the method will be further developed to propose optimal inspection and maintenance strategies by incorporaing the various factors affecting the condition of infrastructure and its stochastic degradation over time. The method will be validated using historical evolution of road and bridge asset conditions. It will result in a probabilistic digital twin based on which we can simulate intervention actions as the third step, such as repairs, upgrades, inspections, and monitoring system installations. The effects of such interventions will be modeled as an advancement of the digital twin in a fourth step, including improvements to asset condition, delays in deterioration processes, and uncertainty in damage detection. The direction that we intend to explore is developing a novel AI pipeline for this purpose to model the effects as key performance indicators. AI models will interact with the digital twin to generate and optimize policies on when and where to inspect and perform maintenance, based on expected degradation and meeting multiple sustainability goals. For the AI pipeline, the potential for obtaining training data to establish key performance indicators from simulations will be explored, such as traffic changes, carbon emissions, and climate change risks.
Deadline : 29 May 2024
(46) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Towards responsible and explainable 6G networks
Join the frontier of innovation in 6G: the future of mobile networks technology! In the Netherlands, a unique alliance of 60 top-notch ICT companies, semiconductor firms, and research institutions has united to spearhead specific aspects of 6G: (1) software antennas, (2) AI-driven network software, and (3) groundbreaking 6G applications. Join us as a PhD student in this prestigious Future Network Services (FNS) flagship project, where research and entrepreneurial pursuits converge.
Various 6G vision papers have underlined the social value that’s meant to be derived from 6G; think of reducing digital divides within and between countries, building trust, etc. As it stands, though, the telecom industry itself does not have the structures in place to cater to this. How can we encourage ethical outcomes and embed responsibility by design in developing 6G? How do we navigate design trade-offs responsibly? How to assure the responsible and explainable use of AI in 6G? Thus, analogous to “responsible AI”, can systems and technologies be developed that support “responsible 6G” design? Your PhD research will explore just that. This PhD offers ample freedom to pursue your research interests, be it in responsible AI, cybersecurity, privacy-by-design, etc. for mobile networks, and in that process we will not only consider functional requirements (like throughput and latency), but also non-functional requirements related to norms and values. For example privacy-preserving and bias-free design.
Become the catalyst for responsible innovation! Your research isn’t just about asking questions; it’s about forging a path toward 6G that prioritises ethics. Your home base will be the Networked Systems group, where you will be supervised by Prof. Fernando Kuipers (professor of computer science). You will be co-supervised by Jeroen van den Hoven (professor of ethics). Hence, you get to spar with two groups. In this highly interdisciplinary, collaborative, and friendly atmosphere, we will give you all the support you need to develop and grow.
Deadline : Open until filled
About Delft University of Technology (TU Delft), Netherlands –Official Website
Delft University of Technology, also known as TU Delft, is the oldest and largest Dutch public technical university. Located in Delft, Netherlands, it is consistently ranked as one of the best universities in the Netherlands, and as of 2020 it is ranked by QS World University Rankings among the top 15 engineering and technology universities in the world.
With eight faculties and numerous research institutes, it has more than 26,000 students (undergraduate and postgraduate) and 6,000 employees (teaching, research, support and management staff).
The university was established on 8 January 1842 by William II of the Netherlands as a Royal Academy, with the primary purpose of training civil servants for work in the Dutch East Indies. The school expanded its research and education curriculum over time, becoming a polytechnic school in 1864 and an institute of technology (making it a full-fledged university) in 1905. It changed its name to Delft University of Technology in 1986.
Dutch Nobel laureates Jacobus Henricus van ‘t Hoff, Heike Kamerlingh Onnes, and Simon van der Meer have been associated with TU Delft. TU Delft is a member of several university federations, including the IDEA League, CESAER, UNITECH International and 4TU.
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