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 Autonomous Reasoning on Robots Operating in the Real, Open World
You will work within the OpenBots laboratory creating autonomous robots that operate in the real world with various other PhDs and engineers. The OpenBots laboratory aims to develop a common sense capability for robots making them able to understand their surroundings and plan proper actions to execute their inspection task, even when part of their surrounding world is unknown and dynamic. The inspection task deals with a robot walking around a perimeter and, meanwhile, detecting abnormal events based on vision or sound, after which a detailed investigation of the siutation might be required. The common sense capabililty that we develop shall allow the robot to complete similar inspection tasks in different (potentially changing) environments, such as governmental buildings, industrial plants, or train tracks. Our developments shall follow the latest achievements in artificial intelligence, world modelling, knowledge representations, mission planning and motion planning, where each team member has its own specialization.
Your research will be related to world modelling and knowledge representation, i.e., creating the robot’s situational awareness, assuming that it is able to detect and observe individual objects and events through its camera and microphone. More specifically, you will study and develop algorithms for managing the robot’s information based on these observations and based on a predefined, or learned, knowledge structure. Since our robots will operate in the real, open world, incoming information will be corrupted by noise and mistakes. Therefore, it is important that information is managed by combining evidences related to the observations with logic and symbolic reasoning, like a Bayesian network or Markov logic networks. However, to cope with the unknowns it might be essential to replace Bayes’ principles with, for example, possiblity theory. In addition, we expect to develop deductive reasoning so that when the robot encounters an unknown situation it may rely on general knowledge/models, and plan experiments to either accept or falsify the application of such knowledge/models to the unknown situation. Finally, you will also study and develop algorithms that extract a situational graph representing the current situation of the robot, or its near-future. Situational graphs shall be used for task planning of the robot and should thus contain which task can be carried our where, for example through inductive reasoning on the available information along with patterns in such information that may be linked to a task-capability of the robot.
The OpenBots project is being carried out in a team of 5 PhD students, 3 at the Delft University of Technology (where this vacancy is one of such PhDs) and 2 at the University of Amsterdam. You will work 3 days a week at the university and the other 2 days you will work with all other 5 PhDs at one of the external partners being TNO and the Royal Netherlands Marechaussee where a physical lab environment is available. The project is carried out with supervisors from the Cognitive Robotics Group of the Delft University of Technology (Dr. J. Sijs, Dr. C. Hernandez Corbato and Prof. J. Alonso-Mora) and the Video and Image Sense Lab of the University of Amsterdam (Prof. Cees Snoek).
Deadline : 19 January 2025
(02) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Developer-centred Just-in-Time Test Generation
Software developers are asked to write comprehensive tests for the code they are creating, which can be a tedious and time-consuming task for them. To help them, researchers are developing a variety of automatic test generation tools. However, current state-of-the-art test generation methods often require the code under test to be fully completed before tests can be generated. This conflicts with our requests to developers to create tests directly when they are writing their production code.
To support developers, we aim to develop tools that closely collaborate with them by studying the code they write and directly proposing relevant generated tests. This type of Just-in-Time test generation would enable many new possibilities to create high-quality software with comprehensive and effective tests early in the programming process.
As a PhD student in this postion, you will lay out the foundations for Just-in-Time test generation and have the opportunity to design, prototype and evaluate new tools and techniques for test generation.
The technologies you work with will be driven by your interest. This could be test amplification, search-based test generation or generative artificial intelligence and large language models.
To realize Just-in-Time test generation you will work with source code analysis tools and build prototypes for developer IDEs such as IntelliJ, VS Code or others.
In your research, you will first learn to, and later independently… :
– Apply qualitative and quantitative research methods to study the experiences of developers with just-in-time test generation.
– Develop and evaluate new techniques for detecting test-worthy code changes and quickly generating incremental and effective tests.
– Design and conduct user studies to assess the effectiveness of the proposed tool.
– Collaborate with other researchers and developers to strengthen the tool’s practical applicability.
– Communicate your research findings by publishing in and presenting at top-tier academic conferences and journals.
The PhD candidate will be embedded in the Software Engineering Research Group at TU Delft, an internationally recognized research group with a strong focus on quality in software engineering, while encompassing a wide range of software engineering topics like AI for SE, sustainability, DevOps and testing of distributed systems. The candidate will have the opportunity to collaborate with experienced researchers, attend conferences, and experience an international research environment. The position will focus on research, with optional contributions to education and supervision of students.
We are committed to creating a diverse and inclusive research environment and encourage applications from all qualified candidates, regardless of their background or experience. We believe that diversity contributes to a more innovative and creative research community.
If you do not meet some of the optional requirements listed, we encourage you to still apply and explain how your unique experience and skills can contribute to our research.
Deadline : January 12, 2025
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(03) PhD Degree – Fully Funded
PhD position summary/title: PhD Position in the Field of Future Wind Farms Control
Wind energy is crucial for realizing climate neutrality, energy independence, and energy security. Optimal operation of wind energy systems is challenging because of their inherent complexity, fast-changing operating conditions, as well as conflicting requirements for the cost of investment and energy generation. Novel optimization-based controllers have the potential to tackle these challenges. In particular, the controllers leverage the properties of feedback to take into account recent data instead of relying on mathematical models of the system, making them a good candidate for future wind farm control. At the same time, long-term performance of these controllers remains an open question.
Deadline : 5 Jan 2025
(04) PhD Degree – Fully Funded
PhD position summary/title: PhD Position in Microstructural and Fracture Characterization of Welded Green Steels
Green steel is a significant step in upcoming energy transition towards decarburization and sustainable future. We are seeking an ambitious experimental PhD candidate to join EU-funded project MOWSES (https://www.mowses-steel.eu/), which aims to enhance the safe application of green steels in critical European infrastructure by focusing on welded joints.
Future green steels will be manufactured using an increased amount of scrap, likely of lower quality and containing higher concentrations of residual elements. This will modify the chemical composition and thus the response of the material to thermal cycles, such as imposed by welding. There is therefore a need to identify what amount of residual elements can be tolerated in the weld, particularly in the HAZ of future clean steels. The ultimate goal of this project is to create steels with improved weldability, toughness, and strength, even when recycled from lower-quality scrap. Advanced microstructure and mechanical characterization will play a key role for this development, ensuring the steel can meet thorough safety and performance standards.
Your particular focus will be to deeply understand and possibly mitigate fracture nucleation and propagation micromechanisms in welded green steels at various concentrations of residual elements. To address this goal, a multi-scale experimental correlative characterization of microstructural and mechanical properties (e.g., strength and toughness) will be used. You will work closely with various consortium partners, in particular Ocas, Dillinger and University of Ghent.
For this PhD position we are looking for a candidate with master degree in Materials Science or related disciplines, with good knowledge of metallurgy, metal science and experimental mechanics of materials.
Deadline : 15 January 2025
(05) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Signal Processing for Low-power Digital Radars
Currently, automotive radars use fully digital receiver architectures with up to 16 antennas. While increasing the number of antennas enhances angular resolution, fully digital radars with large arrays suffer from high power consumption. This project will explore emerging low-power radar designs and develop signal processing techniques optimized for these architectures.
Signal processing with low-power digital radars is challenging due to the stringent waveform constraints imposed by low-power components. Additionally, these components often lead to severe hardware imperfections that perturb the measurements acquired at the radar receiver. To address these challenges, we seek a talented individual to develop signal processing techniques capable of effectively operating under both waveform constraints and hardware imperfections.
As part of this project, you will tackle this challenge by developing cutting-edge solutions that will push low-power automotive radars to their limits. Specifically, your focus will be on two key research themes:
- A) Designing multi-dimensional radar waveforms that are suitable for low-power radars.
- B) Developing robust signal processing algorithms for low-power radars.
By joining our team, you will contribute to the advancement of automotive radar technology, enabling higher resolution and improved detection performance, all while operating under stringent constraints in low-power radars.
Deadline : 15 January 2025
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(06) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Scale-resolving Simulation of Wind-assisted Ships and Low-fidelity Model Optimization
The maritime industry is under pressure to reach the net-zero Greenhouse Gas target set for 2050 on international shipping to alleviate the climate crisis. A strong collaboration between industry, research institutes, and universities is needed to develop and implement novel technologies to achieve this target.
In response to this challenge, TU Delft recently launched a multi-disciplinary, interfaculty research program on Wind Assisted Ship Propulsion (WASP) focusing on three research themes: 1) Fluid dynamics, 2) Design and operation, and 3) Sustainable and societal impact. We are a young, international and diverse team within the Faculties of Aerospace and Mechanical Engineering. We approach problems in a horizontal “team spirit” and continuously traverse boundaries between theory, simulations, and experiments. We strive for a co-creative and stimulating environment where you can develop further skills as a scientist, team member and teacher. We place great emphasis on a collegial working environment where everyone is welcome and encouraged to shape a stimulating and impactful PhD track. In addition, given the multi-disciplinary nature of wind-assisted technologies, your research will contribute to other projects on different themes within the WASP program.
Deadline : 5 January 2025
(07) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Propeller and Rudder Performance in Wind-assisted Ship Propulsion
The maritime industry is under pressure to reach the net-zero Greenhouse Gas target set for 2050 on international shipping to alleviate the climate crisis. A strong collaboration between industry, research institutes, and universities is needed to develop and implement novel technologies to achieve this target.
In response to this challenge, TU Delft recently launched a multi-disciplinary, interfaculty research program on Wind Assisted Ship Propulsion (WASP) focusing on three research themes: 1) Fluid dynamics, 2) Design and operation, and 3) Sustainable and societal impact. We are a young, international and diverse team within the Faculties of Aerospace and Mechanical Engineering. We approach problems in a horizontal “team spirit” and continuously traverse boundaries between theory, simulations, and experiments. We strive for a co-creative and stimulating environment where you can develop further skills as a scientist, team member and teacher. We place great emphasis on a collegial working environment where everyone is welcome and encouraged to shape a stimulating and impactful PhD track. In addition, given the multi-disciplinary nature of wind-assisted technologies, your research will contribute to other projects on different themes within the WASP program.
Deadline : 5 January 2025
(08) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Benchmark Model Tests and Performance Assessment of Appendages for Wind-assisted Ships
The maritime industry is under pressure to reach the net-zero Greenhouse Gas target set for 2050 on international shipping to alleviate the climate crisis. A strong collaboration between industry, research institutes, and universities is needed to develop and implement novel technologies to achieve this target.
In response to this challenge, TU Delft recently launched a multi-disciplinary, interfaculty research program on Wind Assisted Ship Propulsion (WASP) focusing on three research themes: 1) Fluid dynamics, 2) Design and operation, and 3) Sustainable and societal impact. We are a young, international and diverse team within the Faculties of Aerospace and Mechanical Engineering. We approach problems in a horizontal “team spirit” and continuously traverse boundaries between theory, simulations, and experiments. We strive for a co-creative and stimulating environment where you can develop further skills as a scientist, team member and teacher. We place great emphasis on a collegial working environment where everyone is welcome and encouraged to shape a stimulating and impactful PhD track. In addition, given the multi-disciplinary nature of wind-assisted technologies, your research will contribute to other projects on different themes within the WASP program.
PhD Position: Benchmark towing tank tests for wind assisted ships and performance assessment of auxiliary appendages
To predict the performance and the forces acting on wind assisted vessels Computational Fluid Dynamics (CFD) tools are increasingly applied. However, experimental validation remains crucial for refining these predictions, particularly in complex hydrodynamic scenarios. This PhD position focuses on using towing tank tests and high fidelity flow field measurements using Particle Image Velocimetry (PIV) to enhance our understanding of wind-assisted ships and the performance of auxiliary appendages under various conditions.
The goal of this research is to conduct detailed model-scale tests to support the development of high-fidelity CFD tools, especially for ships operating at drift angles with additional appendages. This includes both validation of CFD codes and assessment of the impact of various appendage configurations on flow dynamics and performance.
Deadline : 5 January 2025
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(09) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Microstructural Modelling of Steel Fracture
We are seeking an ambitious PhD candidate who will help us to make steel more environmentally friendly by identifying the effects of recycling on the toughness of the steel.
A prior PhD study developed a statistical model that relates certain aspects of steel microstructure (e.g. grain size, size and properties of subgranular particles) to the low-temperature toughness of the material. In this project, you will extend the work by introducing a microstructurally-informed model for the high-temperature toughness into the same material. You will then update both the low and high temperature toughness models with mechanical behavior of subgranular particles based on their chemical composition. You will work closely with materials scientist within TU Delft, RWTH Aachen and University of Gent and with data scientists at University of Saarland. Your main focus will be in the use of the experimental data from the materials scientists to develop the models and upscaling to macroscopic fracture toughness data in the ductile to brittle transition. While you will work primarily in modelling, direct collaboration with the experimental part is possible (e.g. attending tests), and limited experimentation is possible based on preference.
Deadline : 5 Jan 2025
(10) PhD Degree – Fully Funded
PhD position summary/title: PhD Position in the Design of Bio-Hybrid Interactive Electronics
Biodesigners are rapidly uncovering a wealth of unique possibilities in aesthetics, functionality, and sustainability that living organisms, such as bacteria, offer for interactive systems. However, integration with digital technologies, which offer their own set of interactive possibilities and also are the foundation of the current systems that are pervasive in society today, remains a challenge. This is largely in part because the computational architectures upon which modern interactive systems are built prioritize metrics such as speed, size, power, and stability, in turn demanding that any interfacial modules have well-defined and stable electrical characteristics. Such paradigms are fundamentally at odds with living organisms, which are inherently variable, dynamic, and transient. As a result, although research in both biodesign and computational systems continue to produce innovative interactive systems, these endeavors, and their respective communities of researchers and practitioners, largely exist on separate tracks, with their own set of design values, goals, and capabilities.
We are seeking a highly motivated PhD student to play a formative role in bridging the gap between the world of biodesign and conventional electronics systems design. We seek to create a novel class of bio-hybrid interactive electronics, particularly exploring how we can re-purpose, or even intentionally misuse, modern electronics parts to cater to the needs and requirements of living organisms, while establishing new modes of interaction and a vocabulary to facilitate the seamless integration of living and computational technologies.
Deadline : 3 Jan 2025
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(11) PhD Degree – Fully Funded
PhD position summary/title: PhD Position on the Energy Transition – Business Dynamics of Decentral Flexibility Designs
Decentralized flexibility is pivotal to the energy transition. Numerous companies, start-ups, pilot projects, and not-for-profit organizations offer decentral flexibility solutions to integrate renewable energies, provide storage capacity, balancing power and resilience for electricity grids. Decentral flexibility designs include, among others, peer-to-peer energy trading, flexibility aggregation of home batteries, heat pumps, and electric vehicles, as well as energy communities. These designs represent different pathways to achieving decentralized flexibility, each offering distinct benefits. While some of these designs have proved successful, others have not.
This PhD project investigates the systemic factors that cause decentral flexibility designs to succeed or fail in European energy markets. The aim is to analyse whether a convergence towards certain designs can be expected and where tipping in the diffusion could emerge. The PhD thesis should result in a set of policy recommendations for companies and governments to further stimulate the spreading of decentral flexibility. A mixed-method approach is foreseen for the PhD thesis. The methods applied in this PhD project are a market and literature review, System Dynamics modelling, and empirical analysis.
The candidate is expected to contribute to the stimulating intellectual environment at the Faculty of Technology, Policy, and Management at TU Delft and to support its portfolio of teaching and knowledge transfer activities. Supervision of the PhD project will be shared by Dr. Özge Okur and Dr. Merla Kubli, with Prof. Dr. Martijn Warnier as the promotor.
Deadline : 3 Jan 2025
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(12) PhD Degree – Fully Funded
PhD position summary/title: PhD Position AEM Water Electrolysis
Green hydrogen, produced via water electrolysis using renewable electricity, is one of the cleanest fuels and chemicals. The ambitions for producing green hydrogen are huge; EU wants to use 20 million tonnes of green hydrogen in 2030, which is over 100x more than the current worldwide installation capacity. To achieve this rapid growth, we need to make the electrolyzer technology robust and energy-efficient. Within the HyPRO project, part of the GroenvermogenNL growth fund, we are working jointly with academia and industry to accelerate the improvements in electrolyzer technology and valorize the knowledge on electrolysis phenomena in technology.
This vacancy will focus on Anion Exchange Membrane Water Electrolysis (AEM WE). While AEM WE is less mature than other electrolysis technologies, it potentially combines the advantages of earth-abundant electrode materials (due to working at alkaline environment) and allowing to dynamically operate (due to the low crossover in the dense membrane). However, more understanding is needed for its stability and energy efficiency. In this project, we will focus on enhancing the contact between the electrode materials and anion exchange membrane, to minimize energy losses and create a robust reaction environment. You will explore the use of different ionomer structures and ionomer integrations, and develop design strategies to enhance the stability and efficiency of the AEM WE cell. You will make use of in-operando measurement techniques to map the water management and local concentrations, to better understand the reaction environment and local mass transport.
In this PhD project, you will collaborate closely with 4 PhD’s, 9 postdocs and 15 industrial partners working on AEM WE within the HyPRO project. Your daily operation is in the David Vermaas research group, where our group of ~10 PhD’s and postdocs are working together and sharing work on electrochemical flow systems, including applications of electrolysis, water technology, CO2 capture and flow batteries. The work will also contribute to TU Delft’s e-Refinery institute on electrochemical synthesis that includes >20 principal investigators across the campus, where electrochemical advances are used and valorised in upscaled prototypes, in collaboration with industrial partners.
Deadline : 20 Dec 2024
(13) PhD Degree – Fully Funded
PhD position summary/title: PhD Position DNS of Electrochemical Bubbles – Bubble Detachment in Micro-Structured Electrodes
In the transition to a green economy, hydrogen is called to play a central role to decarbonize hard-to-abate sectors. To this aim, water electrolysis is the key technology enabling the conversion of (green) electricity into hydrogen. However, this pivotal process still suffers from low efficiencies due to the sub-optimal bubble release from the electrodes, which block the reaction sites. Our long term vision is to design advanced electrode geometries that optimize bubble release. In this project we want to develop new numerical techniques to simulate bubble detachment from gas-evolving electrodes in order to advance our understanding of how fluid mechanics, mass, heat and charge transfer impact the overall process performance.
Deadline : 5 January 2025
(14) PhD Degree – Fully Funded
PhD position summary/title: PhD Position IDEE Progression and Retention of Knowledge and Skills
TU Delft engineers are expected to contribute to complex practical challenges by designing and investigating cutting-edge solutions to real-life problems. Educating this next generation of engineers requires not only instilling specific knowledge from individual courses, but also integrating these knowledge blocks into a meaningful, richly connected cognitive schemes of knowledge and skills. In the end, various aspects of teaching at TU Delft can and must be designed, implemented, evaluated, and optimized for a seamless continuum of education. The Teaching Academy of the TU Delft has identified this integration, engineering students’ progression and retention of knowledge & skills, as one of the themes in the “Initiative on Innovation in Delft Engineering Education” (IDEE).
The goal of this PhD project is to explore how that seamless integration can be approached and achieved. As a PhD candidate, you will contribute to:
- Conceptualizing the notion of ‘progression and retention’ of knowledge and skills
- Identifying factors that impact progression and retention
- Designing interventions based on these factors and investigating their effects
- Examining generalizability of the interventions from a single course to different courses even across programs and faculties in addition to promoting interdisciplinary integration for better student learning outcomes.
You will work in a multidisciplinary team of TU Delft staff members and two post-docs in four work packages within the ‘Progression and Retention’ theme.
Deadline : 7 Jan 2025
(15) PhD Degree – Fully Funded
PhD position summary/title: PhD Positions in Scientific Machine Learning for Extreme Fluid Dynamics
Climate change and the race to decarbonize our society is making extreme events in fluids more prevalent. These are rare events where the flow suddenly takes extreme states far from its normal state. Among these, one can cite extreme atmospheric events leading to intense draught, rogue waves capable of capsizing boats, or flashback events in hydrogen-powered clean combustors.
Currently, we cannot accurately predict such extreme events due to the chaotic nature of the underlying turbulent flows and the complex multiscale nonlinear interactions at the origin of such extreme events.
In this project, three PhD candidates are sought to explore and develop cutting-edge scientific machine learning techniques that blends deep learning with physics-based techniques to control such extreme events in turbulent flows. Specifically, three capabilities are targeted:
(i) the identification of precursors and data-driven investigation of the mechanisms of extreme events using a blend of physical simulations and explainable AI techniques;
(ii) the forecasting of the turbulent flow before and throughout the extreme events using physics-constrained data-driven models able to self-correct;
(iii) the control of these flows to prevent extreme events, through a blend of model predictive control and deep learning techniques.
These capabilities will be developed and assessed on a set of flows of increasing complexity, up to an engineering-relevant flow, using high-fidelity simulations for data generation.
The successful candidates will develop this new hybrid scientific machine learning/physic- based framework. The candidates will be part of the European ERC-StG project CONTEXT. They will also be members of the AI Fluids lab and work in collaboration with experienced researchers and other PhD candidates specializing in turbulent flows and artificial intelligence at the Aerodynamics Group of TU Delft.
Deadline : 15 January 2025
(16) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Chemical Engineering: Technology for Electrochemical Membrane Processes
The energy transition impacts all energy- and chemistry-related processes. Two rapidly growing fields in this sector are 1) and conversion of renewable electricity into synthetic chemicals and fuels, such as green hydrogen, and 2) and the electrification of chemical plants. The scales of these processes are astronomical. The chemical industry is responsible for >10% of fossil fuel consumption in EU, from which roughly half the energy is spend on separation processes, and 90-95% of these separation processes are currently thermally driven (i.e., burning fossils). In the transition to renewable energy, electrical-driven separation processes are required at huge scale. Moreover, the electrolyzer capacity is expected to increase from the present-day 40 GW for the EU by 2030, which is >5% of the total EU’s primary energy consumption.
Electrochemical conversion, for example as CO2 electrolysis, is playing a crucial role in harnessing renewable energy to form chemical bonds. However, electrochemical technologies for making sustainable chemicals, such as CO2 electrolysis, are still to be upscaled and intensified. Mass transport, water management and stable membrane materials are pivotal in making this electrochemical technologies scalable and perform at industrial standards. In this project, we will explore to use a new strategy, using multilayer ion exchange membranes, to target the insufficiencies in selectivity, water management and catalyst interaction. You will develop new types of polymer-based membranes, using an hierarchical layered approach, to address water transport, ion selectivity and conductivity in separate layers. You will develop integrated membranes structures to allow water channels at microscale, and introduce layers of porous, capillary-active materials to distribute the water to the reaction spots. You will also study the impact of different ions and membrane chemistry on the selectivity and rate of the electrocatalysis reaction. Finally, you will implement high-tech optical techniques to map the flow and concentration of reactants inside an operating electrochemical cell.
This PhD project is part of the NWO-funded Vidi project. You will collaborate closely with another PhD candidate in this project (working on developing new membrane materials) and 4 industrial partners. Your daily operation is in the David Vermaas research group, where our group of ~10 PhD’s and postdocs are working together and sharing work on electrochemical flow systems, including applications of electrolysis, water technology, CO2 capture and flow batteries. The work will also contribute to TU Delft’s e-Refinery institute on electrochemical synthesis that includes >20 principal investigators across the campus, where electrochemical advances are used and valorised in upscaled prototypes, in collaboration with industrial partners.
Deadline : January 4, 2025
(17) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Multiscale Simulation of Deformable Fractured & Faulted Reservoirs
Subsurface geological formations contribute significantly to decarbonization of energy sector and climate action. To achieve a green net-zero world, carbon dioxide is expected to be stored in the subsurface formations by 3-10 Gt per year. Moreover, 20 Mt of green hydrogen per year is expected to enter the EU energy market by 2030. Such a transfer towards green gas can happen successfully if large-scale storage technologies are also developed. For that geological porous reservoirs and salt caverns are seen as the viable options. Safe and effective utilisation of these formations require advanced simulation techniques, which allow for multiscale simulations of highly heterogeneous and fractured porous media. We have successfully developed pEDFM method for hydro-thermal flows in fractured media, with heterogeneous properties for both matrix and fractured domains [Tene et al., AWR 2017; HosseiniMehr et al., AWR 2022]. These methods have been also integrated within our Adaptive Dynamic Multiscale Framework (ADM) [HosseiniMehr et al., JCP, 2018].
In this project, we aim to extend the pEDFM-ADM to include deformation of fractures, i.e., geomechanics. This would allow for analyses of pore pressure fluctuations on the stability of the geo-system, especially the fractures and fault activations. We aim to follow an embedding strategy, similar to pEDFM, but now devised for mechanical deformation.
The PhD researcher will be required to implement the advancements in our open source inhouse DARSim (https://gitlab.com/darsim) simulator so the stakeholders from universities to industries take advantage of it.
Deadline : 15 Jan 2025
(18) PhD Degree – Fully Funded
PhD position summary/title: PhD on Mechanical Recycling of post-Consumer Thermoplastics for Architectural Applications
The principal hypothesis of this PhD is that various contaminated plastic waste streams can be transformed into high-quality architectural components and is focused on larger volume thermoplastic waste streams that contain challenging to remove contaminants. To prove this hypothesis, a deep understanding is required of the interrelationships between (i) the characteristics of distinct plastic waste streams, (ii) the selected processing methods, (iii) the morphology and structure of the resulting recycled material, (iv) the mechanical properties of the recycled material and (v) the grading of the obtained material and its properties into suitable architectural applications. The fundamental overarching knowledge gap is the correlation between the manner that distinct types of contaminated plastic waste are mechanically recycled to the resulting material micro-, meso- and macro- level structure and associated material properties.
To investigate this, the thesis requires a significant amount of laboratory work, with the aim to: (i) map the current material flows and challenges of plastic waste, (ii) characterize the selected waste streams, (iii) investigate suitable production processes and optimum processing parameters, (iv) evaluate the obtained material structure using applicable material characterization techniques, (v) mechanically test the recycled materials and identify critical contaminants, (vi) combine all the above data to form a cause-effect correlation between waste type, processing, recycled structure and mechanical properties and (vii) suggest suitable architectural applications for the mechanical and visual properties of the obtained recycled materials.
In this manner, the project aims to shed light on the creation mechanisms and mechanical behaviour of different inhomogeneous meso-level structures in recycled plastic materials. Defining this relationship will provide engineered recycled plastic materials within a predictable range of material properties. This will contribute to unlocking the use of recycled plastics in the building sector, thereby leading to significant savings in raw materials and carbon dioxide emissions and preventing plastic waste from leaching into the natural environment and impacting our health.
You will hold an appointment at the Structures & Materials section, Restruct Group, Department of Architectural Engineering + Technology under the supervision of dr. Telesilla Bristogianni, dr. Faidra Oikonomopoulou and Prof. dr. Mauro Overend. The position is funded for a duration of 4 years, during which you will (i) undertake research on your PhD topics and (ii) as
Deadline : 2 February 2025
(19) PhD Degree – Fully Funded
PhD position summary/title: PhD Position AI/ML Algorithms for Next Generation Transportation Systems
Do you aspire to conduct groundbreaking AI/ML research while collaborating closely with Amazon Science Teams? Join us and make a real-world impact!
Efficient and reliable transportation systems are critical to the success of global e-commerce and vital for the functioning of modern societies. As these systems grow more complex, challenges such as optimizing delivery routes and capacity planning, managing dynamic demand under stringent deadlines, and ensuring cost-effective and sustainable logistics become increasingly intricate. AI and ML provide a powerful foundation for addressing these challenges through data-driven models, principled optimization techniques, and scalable solution algorithms capable of handling uncertainties inherent in real-world systems.
In Project Hermes, TU Delft and Amazon join forces to design the next generation of AI/ML-enabled transportation solutions, with a focus on efficiency and sustainability. As a PhD researcher, you will drive algorithmic innovations for transportation systems, utilizing and extending AI/ML tools as well as cutting-edge optimization techniques, towards creating solutions that are robust, principled and scalable. The studied problems will be motivated by real-world use cases and evaluated in representative scenarios, in collaboration with scientists from Amazon Science, EU Logistics.
Deadline : 12 Jan 2025
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(20) PhD Degree – Fully Funded
PhD position summary/title: PhD on Integrating Resilience and Sustainability for Future-Proofing Urban Infrastructure Systems
The Department of Engineering Systems and Services at the Delft University of Technology (TU Delft) is seeking a highly motivated and talented PhD student to join our research team.
The increased severity and frequency of extreme weather events have substantial impacts on the built environment and pose significant societal risks. In many cities, these risks are compounded by aging infrastructures (roads, water distribution systems, and energy grids among others), evolving (inter)dependencies, and growing demand from rapid urbanization. This is particularly true in cities of the Global South, where infrastructure development and management have historically been haphazard and uncoordinated, resulting in more pronounced impacts of climate extremes in recent years. Therefore, aligning infrastructure growth, replacement and management with the transition to sustainable and resilient cities becomes crucial, to enabling urban infrastructure systems to meet user expectations, emission targets and withstand unanticipated shocks.
However, research on sustainability and resilience within the context of infrastructure asset management remains fragmented. Infrastructure operators often prioritize short-term needs and lack clarity regarding system-level, long-term sustainability transitions. Additionally, there is ample empirical evidence for systemic risks that stem from cross-infrastructure dependencies. Understanding these cascading risks and integrating them into management approaches will be instrumental in making our infrastructure systems futureproof under resource constraints.
The aim of this position is to develop the theoretical and empirical foundations to address the emerging uncertainties to manage sustainable and resilient urban infrastructure systems, especially in cities that are infrastructure-deficient (e.g., in Global South) or have inadequate resources for systematic maintenance.
As a successful candidate, you will develop the research and related use-case(s) based on an interconnected urban infrastructure network at a city or regional level. The infrastructure systems that you may consider include multimodal transportation/freight networks or interconnected urban utilities (such as road networks and drainage networks). After identifying gaps in the current state of the art, you will develop a theoretical framework to model infrastructure performance and demand fulfilment under various extreme weather disruptions, climate change scenarios, and demand stresses. You will calibrate or validate the theoretical model using infrastructure performance datasets. For example, you will use datasets from pavement asset management systems (PAMS) along with open-source weather datasets, regional traffic projections, and land use datasets in the study region(s). You will apply the theoretical models on possible future scenarios and develop cross-infrastructure resource allocation strategies to achieve sustainability and resilience objectives. You will also have the opportunity to develop novel infrastructure system performance datasets using ML/AI tools to advance your research. The outcome of this PhD research is analysing how investments in resilience and sustainability can support the broader societal role of infrastructure systems in the future, especially in resource-deprived contexts. This research will help inform policy at various levels, aiming to improve existing infrastructure management practices, especially in infrastructure-deficient settings, with a long-term view.
We are looking for a creative and autonomous candidate, who is keen to combine methods and approaches from different fields, such as network modelling (focusing on transport networks, energy grids, or water distribution systems), geospatial/urban analysis, and policy-oriented simulation or optimization methods.
Deadline : 15 January 2025
(21) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Foundation Models for Autonomous Vehicle Perception
Autonomous vehicles have been shown to underperform when deployed on conditions that differ substantially from the training conditions, so-called domain gaps. These domain gaps can include deployment in different regions, weather or using different sensors. Numerous domain adaptation methods have been proposed to bridge these domain gaps and let the model operate well on the target data. In this project we seek to find an intermediate representation for data coming from varied sources. By bringing new data into this representation, we overcome domain gaps and are able to train models that are robust to different conditions, also referred to as foundation models. While foundation models have achieved widespread success on images and text, currently few exist for lidar and radar data, making this a promising research direction. These foundation models will bring superior performance and allow us to utilize data from varied sources, thus reducing the data collection and labeling costs.
The Intelligent Vehicles group at TU Delft, the Netherlands, invites applications for a fully funded 4-year PhD position in the area of foundation models for autonomous vehicles. This position is partially funded by the EU Horizon project Cynergie4MIE, as well as by internal funding sources. Due to this combination, the topic for the last 1.5 years is somewhat flexible. Large Language Models and/or Gen AI are two potential topics, as long as they fit into the overall thesis direction.
Your results will be published in top tier conferences like CVPR, ICCV, ECCV, ICRA and NeurIPS. For your work you will have access to the compute resources of TU Delft, ranging from personal machines, to shared GPU servers, the Delft AI Cluster that is shared across departments, as well as DelftBlue, which is one of the top 250 supercomputers in the world. Your main supervisor will be Dr. Holger Caesar, creator of the nuScenes dataset and co-author of the PointPillars method for lidar-based object detection. You will receive hands-on mentoring for your career development.
Deadline : 22 Dec 2024
(22) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Composite Materials with Architected Discontinuities
Unidirectional composites with discontinuous fibre architectures may offer processing advantages to manufacture thin shells with nonzero Gaussian curvature, like in for example pressure vessels. Furthermore their discontinuous structure offers challenges but also benefits in mechanical performance. The aim of this project is to investigate the mechanical response of novel thin ply thermoplastic unidirectional tapes with fibre discontinuities at the micro and mesoscale and to derive their constitutive laws towards the macroscopic scale.
In this position you will contribute to this need, by developing and validating experimental methodologies to assess the influence of discontinuities at various length scales for laminate architectures utilizing thin plies. Supported by analytical or numerical modelling you will develop physical theories substantiating constitutive laws to be developed. This project will feature a large amount of experimental work, in combination with analytical and/or numerical modelling, depending on which approach proves to be most feasible.
You will conduct this PhD research within the Aerospace Structures and Materials Department of the Faculty of Aerospace Engineering of TU Delft. Direct supervision will be provided by Dr.ir. R.C. Alderliesten, Dr. ir. J.M.J.F. van Campen and Prof.dr. C.A. Dransfeld.
This position is a part of the ‘Luchtvaart in Transitie project. You will therefore collaborate with other PhDs and post-docs, and communicate with industrial partners. Within the ASM department there is currently a larger community of PhD candidates and post-docs working on related activities.
Deadline : 1 Jan 2025
(23) PhD Degree – Fully Funded
PhD position summary/title: PhD Position on Human Autonomy for AI Safety
A major challenge in crises is the combination of complexity, time pressure and moral decisions. AI has the potential to support first responders in crisis decisions, yet the increasing use of AI has led to a debate about the risks and AI safety if decisions are automated and supported by AI. In this PhD project, we go beyond the ‘human in the loop’ paradigm, and investigate the implications of automation at scale in human-AI teams. This PhD is part of the NWO-funded AI-COMPASS project.
In human-AI-teams, often, the distinction between tasks to be automated (or not) are made via simplified guidelines (‘humans are better at’ / ‘machines are better at’), yet not taking into account team dynamics and trust. A hallmark of control and oversight of automated systems is the idea of human agency. However, in complex and decentralized networks, the implications of automation will emerge from the interaction of many humans with many AI systems, which is currently not considered.
This PhD position aims to assess these aspects of AI risk by contextualising automation and integrate group and team performance, where many humans are supported by AI. Key questions are: which tasks or processes can and should be automated? How can we maintain meaningful human autonomy and control in decentralized networks?
To address these questions, you will start from developing a conceptual model of human-AI interaction in decentralized networks based on the literature and empirical data collected through workshops with our stakeholders and partners. From there, you will design and implement an agent-based model that will integrate human decision-makers from different organisations (e.g., police, municipality) and artificial agents that support these humans by automating information acquisition, analysis, or decision-making. This model will then be used to analyse a range of crisis scenario for our two most prominent use cases in The Hague and Rotterdam. The insights from the simulations will then be coupled to the concept of human moral autonomy, which defines the conditions that need to be fulfilled for human decision-makers to be able to maintain moral agency when interacting with an AI. The results will lead to coordination guidelines for human-AI-teams in crisis response that detail which processes to automate, and under which circumstances.
The position is a part of the AI-COMPASS team of three PhD researchers that will collectively research crowd crisis management at the TU Delft. This position is embedded at the Faculty of Technology, Policy & Management (TPM), and will be jointly supervised with the faculty of Civil Engineering. As such, the candidate will be joining a vibrant and growing community. You will be supervised by Prof. Tina Comes, Dr. Srijith Balakrishnan and Prof. Serge Hoogendoorn, and you will closely collaborate with Dr. Sascha Hoogendoorn-Lanser. This embedding ensures access to a broad network of partners in research, policy and practice. You may also gain experience in supervising MSc students and engage in teaching and training. Via our network and tailored mentoring, we will create opportunities for you to develop your career through support for conferences, collaborations, and training possibilities.
Deadline : 14 Jan 2025
(24) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Wide Area Protection and Control Applications
The decommissioning of fossil-fuel generation and increasing integration of power electronics-based renewable resources, the application of HVDC, and storage devices, result in scenarios leading to power system oscillations with a broad frequency spectrum, decreased system strength, and low inertia. This raises important questions on how the system will deal with such a new normal that may easily lead to emergencies resulting from overload, (under/over)voltage, and angle variations.
The principal constraining factor on network capacity is the thermal limit of the conductor and therefore, there has been widespread testing and implementation of both sensor-based and sensor-less Dynamic Line Rating technology (with appropriate protective relaying), which monitors the Dynamic Thermal Rating of the line, based on current-, temperature-, or sag measurements resulting from loading and weather conditions.
In this project, attention will be paid to Dynamic System Rating comprising Dynamic Line and Dynamic Power Rating by applying Synchrophasor-based technologies by applying real-time simulation and monitoring platform. The applied methodology Dynamic System Rating will be Autonomously Controlled.
The project is financed by industry and it is in the scope of the Power System Protection Centre (www.tudelft.nl/pspc) with TSO TenneT and General Electric Venova on board.
Deadline :19 Jan 2025
(25) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Testing Blockchain Applications
Blockchains are the backbone technology of many critical systems, including cryptocurrencies. However, the implementation of blockchain systems and applications is fraught with challenges. They are susceptible to software bugs arising from unexpected executions involving asynchrony, message delivery delays, and network or process faults.
Ensuring the correctness and reliability of source code implementing blockchains and smart contracts is paramount, and this hinges on effective software testing. Given the complexity and scale of these systems, detecting software errors through manual testing is often impractical and insufficient.
This PhD project seeks to develop advanced automated software testing techniques specifically tailored for smart contracts and blockchain systems, with a particular emphasis on the XRP Ledger framework. The testing techniques will rely on evolutionary intelligence, fuzzing, and learning-based testing to automate the process of finding software errors and identifying buggy program statements.
Deadline : 19 Jan 2025
(26) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Structural Biology of a Minimal Divisome
We seek a PhD candidate to investigate unconventional dynamin-like proteins (DLPs) as minimal divisomes for synthetic cell division. This project is part of the EVOLF (https://evolf.life) consortium, in which we work together with 31 research groups to study if and how we can create living cells from lifeless molecules.
The project involves structural and mechanistic studies of DLP-mediated membrane fission and fusion using cryo-EM/ET, molecular dynamics simulations, and synthetic liposome models. You will explore novel DLP groups identified from metagenome analysis for their ability to mediate reverse topology fission, perform time-resolved structural analyses to unravel their mechanism by cryo-EM/ET imaging, employ protein design to improve their efficacy, and develop a GTP regeneration system to fuel a self-sustained synthetic division machinery. Together, your results will not only shed light on fundamental questions in membrane remodelling but also form the basis to esbtalishing a minimal cell division machinery in synthetic cells. The project will be conducted jointly with leading collaborators, including the laboratories of Cees Dekker, Bert Poolman, Thijs Ettema and Siewert-Jan Marrink. Ideal candidates will have expertise in molecular biology, biophysics, or structural biology and a strong affinity for working on challenging mechanistic questions in structural biology and biophysics.
Your home base will be the Jakobi Lab (https://cryoem.tudelft.nl) at TU Delft | Kavli Institute of Nanoscience. We are a multidisciplinary and internationally diverse team of scientists with backgrounds ranging from cell/molecular biology and biochemistry to (bio)physics. Our drive is to push boundaries in our aim to visualise molecular processes at the highest possible resolution. As part of the Department of Bionanoscience and the Kavli Institute of Nanoscience, we enjoy access to state-of-the-art infrastructure for biochemistry, molecular imaging, cryoEM/ET and high-performance computing. We are also committed to world-class education, to which you will contribute, coaching undergraduate and graduate students. Fostering a friendly, non-hierarchical, and collaborative atmosphere, we support each other by exchanging ideas and knowledge in the pursuit of our ambitious goals. And you will get all the support and training you need for your personal and professional growth.
Deadline :20 January 2025
(27) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Physics-informed Learning and Layered Control Architectures for High-tech Greenhouses
High-tech greenhouses play a crucial role in ensuring sustainable, affordable, and reliable local food production. The construction and operation of high-tech greenhouses is therefore expected to grow significantly over the next decade, but this increase is not matched by a commensurate increase in the number of growers capable of effectively managing them. Achieving high resource-use efficiency of the greenhouse, e.g., maximizing the kg of food produced per unit of energy, requires growers to consider a complex relationship between the short and long term consequences of each operational decision. These factors have lead to a significant interest in autonomous systems for greenhouse management.
Although autonomous greenhouse systems have achieved remarkable improvements in performance over the last couple of years, the current technology cannot be scaled to the entire greenhouse industry as current methods require either enormous amounts of operational data or high fidelity simulators. In addition, current autonomous greenhouse control systems focus almost entirely on the highest level of decision making, while ignoring the lower control layers that actually implement these high-level decisions. This approach leads to a control architecture that is poorly integrated and lacks flexibility in responding to short term perturbations, thereby producing suboptimal performance in terms of profits, resource efficiency, and carbon footprint.
This PhD position is a part of the LEAP-AI project that will address these challenges in autonomous greenhouse control. The project team includes PhD students and researchers at TU Delft, Wageningen University and Research, and Erasmus University Rotterdam, as well as industrial partners that specialize in machine learning, climate control, and computer vision for high-tech greenhouses. The goal of the LEAP-AI project is to collaborate with this team to design the next generation of autonomous greenhouse control systems and will culminate with several large-scale experiments including both research trials as well as validation/demonstration of the new autonomous greenhouse control system in an actual high-tech greenhouse.
Deadline :26 January 2025
(28) PhD Degree – Fully Funded
PhD position summary/title: PhD Position on Biobased Timber Hybrids: Design and Fabrication of Hybrid Structural Systems
Delft University of Technology is hiring a doctoral candidate on the topic of “Biobased timber hybrids: design and fabrication of hybrid structural systems from timber and locally sourced biobased materials”. You will be part of an international team of researchers that works towards more circular, resource-efficient and locally-sourced timber structures.
As a naturally available and variable material, timber holds a lot of potential for creating a resource-efficient, circular and regenerative construction practice. Its mechanical and physical properties are well suited for a variety of applications, not in the least for building structures. The growing demand for multi-storey buildings necessitates the development of high-performance timber components, with outstanding mechanical, acoustic and fire performance. Hybrid solutions can extend the capabilities of timber by fostering synergies with complementary materials. Today, hybrid solutions with concrete or steel are most common. Yet, biobased materials like hemp, flax or earth offer exciting opportunities.
As a doctoral researcher, you will explore the potential of biobased timber hybrids. Your research project will be driven by design and fabrication experiments. Through a hands-on approach you will map the potential and availability of biobased resources in the Netherlands, develop structural concepts for hybrid components of timber and one or more biobased resource streams, and test and validate the developed concepts through physical experiments and 1:1 fabrication of a hybrid structural element. You will consider structural concepts in the wider context of low-carbon and zero-waste design, always informed by the properties and cultivation of the resources. At the same time, you will dive into the tectonics and technical performance, for example by testing connections and bonding techniques to maximise composite action. During the project, you will collaborate with industrial frontrunners in the biobased and timber construction industry.
You will be part of the the ReStruct Research Group in the Structures & Materials section at the Department of Architectural Engineering + Technology and a member of the research team of dr. Stijn Brancart. You will complete a doctoral research under the supervision of dr. Stijn Brancart, Prof. dr. Mauro Overend and Prof. Alex de Rijke. During the four year appointment, you will develop an independent research project and contribute to the research and teaching activities of your group and department.
Deadline : 2 February 2025
(29) PhD Degree – Fully Funded
PhD position summary/title: 2 PhD Positions on the Ethics and Philosophy of the Synthetic Cell
The EVOLF consortium aims to create a living synthetic cell from the bottom up. By doing this, it attempts to answer one of the fundamental scientific questions, namely: what is life? This groundbreaking endeavor needs to include dialogues with society, a dedicated approach for responsible innovation, and philosophical reflections on our understanding and definitions of life. The Ethics and Philosophy Group at Delft University of Technology is developing an integrated approach to address the pressing ethical and philosophical issues accompanying the synthetic cell. Therefore, we are seeking to recruit 2 PhD candidates to work within this project. The candidates will be collaborating with the wider EVOLF consortium, which consists of 31 senior researchers from 5 universities, spanning disciplines such as nanobiology, biophysics and artificial intelligence.
Position 1: Value Sensitive Design
The development of a synthetic cell will produce groundbreaking knowledge on our understanding of life, which may lead to promising new applications, but may also give rise to novel risks. Therefore, this knowledge should be produced in a responsible way. This project aims to align the development of the synthetic cell with relevant societal values, such as safety and accountability.
In the project, the prospective PhD candidate will develop an approach to integrate social and ethical considerations into design choices made in the laboratory. thereby contributing to responsible, fundamental research. The PhD project will be informed by perspectives from a wide range of societal stakeholders through focus groups, interviews and public events. This position will be supervised by Jeroen van den Hoven, Lotte Asveld and Steffen Steinert. Tasks and requirements include:
- Comprehensive literature review to map societal values and concerns about synthetic cell development and identify relevant stakeholders. This requires strong research and analytical skills, a keen eye for detail, and curiosity about ethical issues.
- Conduct focus groups and interviews with external stakeholders, including NGOs, students, religious groups, policymakers, and experts, to create a moral map linking values to specific concerns.
- Collaborate on internal interviews and lab visits with consortium members to compare internal and external values, requiring teamwork, adaptability, and analytical skills.
- Organize workshops for both internal and external stakeholders to validate and refine these design strategies.
Deadline : 5 Jan 2025
(30) PhD Degree – Fully Funded
PhD position summary/title: PhD Position CFD Water Electrolysis
Hydrogen generated from green electricity in water electrolysers is currently widely considered as an essential ingredient for a successful energy transition. Hydrogen bubbles form, coalesce, rise, and move through various parts of the electrolyser, influencing the distribution of flow, current, dissolved gas, and heat. Various problems associated with gas bubbles can arise, including flow maldistributions, the formation of hot spots, and the unwanted crossover of gas. To minimise energy losses and improve safety, you will build a comprehensive computational multiphase flow model including heat and mass transport.
As part of a consortium of Dutch universities and companies (HyPro, https://groenvermogennl.org/project/hypro-onderzoek-naar-technische-risicos-en-kostenverlaging-van-groene-waterstof/) you will look at larger-scale flow phenomena in good communication with a similar effort at AVOXT. At the TU/e a postdoc will work on small-scale bubble release, in collaboration with Veco Precision. Battolyser Systems will develop a CFD model of bubble behaviour within a cell on different electrode types and topologies.
A turbulent Euler-Euler two-fluid model or mixture model is envisioned, coupled to transport of current, heat and dissolved gas. The model will be implemented in commercial software like Ansys Fluent or an open source code like OpenFoam. Working models for various aspects of electrolyser modelling are already used in our groups and may be expanded upon.
You will be part of the lively research groups of Willem Haverkort (http://jwhaverkort.weblog.tudelft.nl) and Johan Padding (https://www.tudelft.nl/en/me/about/departments/process-energy/research/complex-fluid-processing), including a mix of experimental and computational students, PhDs, and other researchers. Occasional work visits or consortium meetings to the collaborative partners are foreseen. Various experimental projects are running in parallel, and you are encouraged to regularly leave your computer, go into the lab and directly compare your results to experiments. Within Delft there is a flourishing community of researchers working on various types and aspects of electrolysis (https://www.tudelft.nl/e-refinery)
Deadline : 5 January 2025
(31) PhD Degree – Fully Funded
PhD position summary/title: PhD Position – Artificial Intelligence for Reuse in the Built Environment
The waste from building demolition presents a significant societal, economic and environmental challenge. Part of this waste consists of fully functional structural components, which can be repurposed for new construction after minimal adaptation. Reintroducing these components into new buildings not only reduces waste but also lowers the CO2 emissions associated with producing new ones. Which strategies are most effective for designing with reclaimed structural components? How can computational methods assist designers in developing more sustainable design solutions?
Current research leverages optimisation methods to allocate reclaimed components within a predefined design. This top-down approach allows only certain parts of the design to be replaced; if no suitable components are available, the designer must rely on newly produced ones.
In contrast, this research explores a bottom-up approach in which design solutions are generated directly and exclusively from the available components. This approach presents unique computational challenges, such as managing the variability in component characteristics and stock composition, efficiently searching the design space, and simultaneously meeting several design requirements. To address these challenges, the research aims to develop innovative computational methods based on Artificial Intelligence (AI). Specifically, it will explore how AI, and particularly reinforcement learning, can support the design of structures that embed diverse reclaimed components while adapting to varying stock compositions.
The research will focus on investigating and developing computational methods for sustainable design. Research methods include analising precedents, developing models, conducting case studies, and performing validations. A key aspect of the research involves training AI models to address increasingly complex tasks, such as designing 2D trusses and 3D spatial frames that integrate single or multiple reclaimed component typologies. As part of this process, you will create environments for training the AI models, investigate strategies for representing reclaimed components based on their geometric and mechanical properties, and define metrics to evaluate the structural performance and environmental impact of AI-generated designs. You will later deploy the AI model as a design tool (e.g. as a CAD plugin), and prototype interfaces that enable designers to define design requirements, interact with the model, and compare different AI-generated design propositions within existing digital environments.
You will work in the Digital Technologies section of the Architectural Engineering and Technology department. This section focuses on the development and application of computational methods, tools, and techniques to sense, understand, model, design, and fabricate the built environment. Its approach integrates computational design and geospatial data, with key research areas including AI-supported and performance-based design, human-computer co-creation, design space exploration for decision-making, and human-robot interaction for creative robotics.
As a PhD researcher at TU Delft, you will be supported by leading experts in computational design and AI for structural design, who will guide the conceptualisation and development of the computational methods. You will also receive advice from specialists in engineering and construction technology to address the mechanical properties and integration of reclaimed structural components. Additionally, you will have access to high-performance computing infrastructures for AI training and structural performance simulations.
Deadline : 2 February 2025
(32) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Data-Driven Understanding of World Heritage Cities
The cultural significance of UNESCO World Heritage is conventionally only defined by the experts and decision-makers. In the past decade, however, social inclusion and public participation have been growing as goals in heritage management. As proposed by the 2011 UNESCO Recommendation on the Historic Urban Landscape, it is important to document and map the knowledge from a broader public to facilitate more inclusive heritage decision-making processes. People constantly share on social media platforms their perceptions about the cities that they visit or reside, in forms of texts, images, and videos. The ubiquitous geo-located and time-stamped user-generated data and the current advances in data science and AI makes it possible to understand how people perceive the historical urban spaces at large scale, especially those where World Heritage properties are located. This bottom-up knowledge will be informative both for the conservation and management of existing heritage, and for the recognition and nomination of new heritage.
Your interdisciplinary research will study how data science and AI can help understand the perception of world heritage cities by citizens at global level. You will collect and analyse a global dataset of user-generated data (written texts and/or posted images) from social media platforms in cities in part listed as UNESCO World Heritage properties. You will develop analytical frameworks to study both the spatiotemporal patterns (how the data are distributed in space and developed along time) and the semantic contents (what are being discussed) of the dataset. Using the obtained knowledge, you will be able to offer spatial design frameworks and planning suggestions for heritage conservation and urban [re]development.
As a PhD candidate, you will be based in the Section of Heritage and Architecture within the Architectural Engineering and Technology Department at the Faculty of Architecture and the Built Environment, TU Delft. Within the section, we conduct research focusing on the values that define heritage and how they impact the sustainability of cities. We are keen to explore cross-disciplinary research methods with global collaborations. You will also be working together with colleagues from the Section of Digital Technologies and other AI labs of the faculty and TU Delft. You will work closely with Dr. Nan Bai, whose research focuses on sensing the World Heritage with big data and AI.
Deadline : 2 February 2025
(33) PhD Degree – Fully Funded
PhD position summary/title: PhD Position on Game Engine-GIS-Simulation System for wind and noise Simulations
In this research you will work on developing responsive and high fidelity urban wind and noise simulation models in a game engine-based urban Digital Twin. You will employ the physics and rendering engines of game engine for optimized performance of computationally intensive simulation models. This research also includes the incorporation of GIS into the game engine, including spatial DataBase Management System (DBMS), open geospatial standards for interoperability and seamless interaction between the various involved data types.
The project begins with exploring the potential of incorporation of Computational Fluid Dynamics (CFD) wind and Raytracing noise models into the game engine. You will develop a theoretical framework for game engine-GIS-simulation triplet. Further enhancements using parallel processing and GPU integration will also be explored. You will subsequently implement the developed framework and will also validate the simulation results using wind and noise observations.
This is a four-year doctoral (PhD research) appointment. You will be jointly supervised by Dr. Azarakhsh Rafiee and Prof. Peter van Oosterom. You will be a member of the Department of Architectural Engineering + Technology, Faculty of Architecture and the Built Environment.The project will offer opportunities to collaborate with academics from other disciplines, as Aerospace and Applied physics as well as with industrial partners.
Deadline : 2 February 2025
(34) PhD Degree – Fully Funded
PhD position summary/title: PhD Position 24/7 Dynamic Comfort
As part of the Sector-Plan scheme, you will join the Section of Environmental & Climate Design (E&CD) within the Department of Architectural Engineering and Technology (AE&T). Your primary responsibilities as a PhD candidate will include conceptualising, developing, executing, and disseminating your PhD research while enhancing its academic impact in the field of human comfort and related domains. You may also choose to become a research fellow (RF) at the Amsterdam Institute for Advanced Metropolitan Solutions (AMS), where you can test your research ideas in real-world settings with support from the RF community, project coordinators, communication teams, and Dutch municipalities. This position will be supervised by dr. Zhikai Peng and involves collaboration with a multidisciplinary team spanning three other departments Architecture, Urbanism, and Management in the Built Environment within the faculty.
This ‘24/7 Dynamic Comfort’ PhD project focuses on creating safe, inclusive, and playful thermal environments. In alignment with these goals, your supervisory team is dedicated to supporting a positive and fulfilling four-year research journey as you work toward your doctorate. Join TU Delft to contribute to ground-breaking research and advance your career in an internationally renowned academic environment.
In the department of Architectural Engineering + Technology we currently have 6 PhD positions open. Click here to see all 6 vacancies.
Deadline : 2 February 2025
(35) PhD Degree – Fully Funded
PhD position summary/title: PhD Position on the Dynamics of Long-distance Passenger Transport Systems
In the past century, long-distance passenger travel has become increasingly common. While bringing many advantages by means of enhanced mobility and connectivity, it also comes at a cost due to the externalities generated, such as the depletion of finite natural resources, noise pollution and its contribution to climate change. There is however a striking discrepancy between the relevance of long-distance travel for emission reduction goals and the lack of knowledge to support its design, planning and policy making.
In this ERC Consolidator Grant project, called Multi-layer, Multi-modal and Multi-class Air and Rail Systems (3MARS), the research team will develop and test models to capture the market relations and dynamics between supply and demand and amongst (air, rail and bus) service providers in response to policy interventions.
Deadline : 27 January 2025
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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