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56 PhD Degree-Fully Funded at Delft University of Technology (TU Delft), Netherlands

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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 in Quantum Networks

The vision of a Quantum Internet is to provide fundamentally new internet technology by enabling quantum communication between any two points on earth. Such a Quantum Internet will – in synergy with the ‘classical’ Internet that we have today – connect quantum processors in order to achieve unparalleled capabilities that are provably impossible using only classical communication. At QuTech and the Quantum Internet Alliance, we are working on making such a network a reality.

Deadline : Open until filled

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(02) PhD Degree – Fully Funded

PhD position summary/title: PhD Signal Analysis for Biomedical Imaging

We offer an exciting PhD position at TU Delft in the field of biomedical image analysis and data processing. Apply now and contribute to a better reconstruction of complex fiber networks, e.g. nerve fibers in the brain. The position is in the group of Dr. Miriam Menzel, at the Department of Imaging Physics. The group has developed an imaging technique that exploits the scattering of visible light to visualize complex fiber structures in biological tissues. Your main focus will be to analyze the measurement data (light scattering patterns), and develop enhanced image processing and signal analysis tools to improve the reconstruction of fiber structures and to extract additional information from the measured scattering signals, such as tissue composition or fiber sizes. Prior knowledge about the imaging technique and/or biological tissues is not required. You will have access to a large database of measured scattering signals from known fiber structures which you can use to train your algorithm, and to learn how to better interpret the data, recognize patterns, and distinguish between different fiber structures – in the brain as well as in other biological tissues. Computational Scattered Light Imaging (ComSLI) is a highly promising new imaging technique that resolves fiber pathways and their crossings with micrometer resolution. While other scattering techniques raster-scan the tissue with a light beam and measure the distribution of scattered light behind the sample, ComSLI uses a reverse setup: The whole tissue section is illuminated from many different angles and the normally transmitted light is measured, thus enabling much higher resolutions and requiring only standard optical components (LED light source and camera).

Deadline : Open until filled

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(03) PhD Degree – Fully Funded

PhD position summary/title: PhD Position Machine Learning on Graphs

Graphs are playing an ever increasing role in nowadays systems as a flexible tool to model complex systems. In addition, these systems generate a vast amount of data which can be modelled as signals or features over these graphs. This is for instance the case of infrastructure networks such as water, energy and transportation networks but also the case of wind farms, solar grids and IoTs. Consequently, developing and using machine learning tools to process these graph data is more important than ever. Such a tools need not only capture the graph structure of the data but also account for the dynamics of the topology as practical graphs change over time.

Deadline : 30 November 2023 

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(04) PhD Degree – Fully Funded

PhD position summary/title: PhD Position Energy Transition for Port Hinterland Transport

You will investigate alternative scenarios and strategies for the energy transition in the context of Hinterland freight transport. The focus is on new energy carriers for road freight transport, including several options for electrification and alternative fuels. The practical question that inspires the research is: for a successful transition, who should provide which support to which technology, and when? The research will provide scientific evidence to help resolve this problem. For the grounding of your work in practice, we have committed various key stakeholders around the port of Rotterdam. Deliverables will include scientific papers and summary reports for port stakeholders, also beyond the port of Rotterdam. The methods used will build on social sciences (e.g. group model building), economics (e.g. appraisal) and engineering (e.g. optimization, system dynamics). 

Deadline : 25 November 2023

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(05) PhD Degree – Fully Funded

PhD position summary/title: PhD on Membrane Engineering for Electrochemical 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. Membranes are playing a crucial role in separation processes and renewable energy conversion. Ion exchange membranes are used in electrochemical energy conversion (such as hydrogen production) and electrical-driven separations. However, the traditional ion exchange membranes are reaching their limits due to the selectivity/conductivity trade-off and their poor compatibility to catalysts for stable operation at high current density. In this project, we will explore to use a new strategy, using thin-film composite ion exchange membranes, to target the insufficiencies in selectivity, water management and catalyst interaction. You will develop new types of polymer-based ion exchange membranes, using a thin-film approach, and study the ion selectivity, permeability and effectivity in operation with an (CO2) electrolyzer. You will analyze the polymer properties and create a rational design for developing ion exchange membranes that are able to break the current selectivity/conductivity trade-offs. With that, we can prepare the chemical industry and renewable energy conversion technologies for efficient, electrical-driven operation.

Deadline :  20-11-2023

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(06) PhD Degree – Fully Funded

PhD position summary/title: PhD Position Intelligent Decisions for Passenger-Centric Public Transport Systems

Public transport, including trains, buses, and trams, has traditionally been controlled from a viewpoint of operations, with limited consideration for passengers’ needs. Transforming public transport operations towards a more passenger-centric approach is highly anticipated, as it holds the promise of encouraging people to choose more sustainable travel options. Nonetheless, integrating heterogeneous passenger needs into the decision-making processes of public transport and efficiently addressing these integrated problems remains a challenge. We welcome you to join us in tackling this challenge. In this PhD position, you will develop and harness innovative techniques (e.g., reinforcement learning, data-driven decision-making, or machine learning-based surrogate optimization) to solve passenger-centric decision-making problems situated in a dynamic and uncertain environment. Uncertainty can arise from factors such as passenger volumes, rolling stock availability, traffic conditions or a combination thereof. The decision-making problems we are concerned with include, but are not limited to, timetable design, traffic management, passenger information provision, and disruption management, all at the network level. Your home base will be the Department of Transport & Planning. We invite you to join our intellectually inspiring, inclusive and internationally diverse team, where you will find an open, friendly and collaborative atmosphere. We share a drive to develop innovative technologies geared to revolutionize traditional public transport to become a central transport mode of future European mobility. You are encouraged to share your knowledge and ideas and get all the support and training you need to grow your career as a researcher.

Deadline : 19 November 2023 

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(07) PhD Degree – Fully Funded

PhD position summary/title: PhD Position Probability Theory

The PhD position is in the area of probability theory and statistical mechanics. The PhD project is a NWO funded “open competition project” which deals with the study of metastability for dilute spin systems. These are classical models of statistical mechanics with bond disorder, i.e. where deterministic pair interactions are replaced by random variables. The main objective is to develop a robust mathematical approach to study the metastable behaviour of these models. Metastability is a universal phenomenon, where a system spends a long time in a state of “pseudo equilibrium” until it crosses over to a stable state. This project aims at identifying the quantities characterising the stochastic nature of this phenomenon, such as the average and the distribution of the random transition time.   

Deadline : 19 November 2023 

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(08) PhD Degree – Fully Funded

PhD position summary/title: PhD Position in Trustworthy Federated Learning for Distributed AI Applications

Federated Learning (FL) and Distributed Computation allow AI models to be trained across multiple decentralized servers holding local data samples without explicitly sharing the data. It’s a game-changer for data privacy and security but introduces unique challenges regarding trustworthiness, fairness, and robustness. This Ph.D. project centers on creating a secure and trustworthy framework for distributed computation (e.g., FL) within often untrustworthy peers. The goal is to develop a comprehensive system leveraging Distributed Ledger Technology and smart contracts that improves the efficiency of federated learning and enhances the transparency, explainable AI, and accountability of the data processing during data modeling. Beyond the technical aspects, you will also delve into the real-world applications and business implications in the captivating aviation sector. You’ll unravel the complexities of conflicts of interest within aviation, working to find innovative solutions. Your role will involve understanding the needs of different stakeholders, designing new ways of collaboration through distributed governance models, and crafting a novel business approach. For this, you will utilize the readily existing links to KLM-AirFrance and Independent Data Consortium for Aviation (IDCA) to develop the foundation for sharing data in a way that promotes cooperation rather than competition. This framework isn’t just about data – it’s about reshaping how data and knowledge are shared, all while leaving a positive footprint on both the environment and society in multiple use cases. As a Ph.D. researcher in this position, you will work closely with Dr. ir. Marcela Tuler de Oliveira, dr. Dr. ir. Mark de Reuver and Dr. Aaron Ding. There are opportunities to teach and supervise students in collaboration with your supervisors.

Deadline : 15 November 2023 

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(09) PhD Degree – Fully Funded

PhD position summary/title: PhD Position Non-destructive Material Characterisation of Aerospace Composite Materials Using Ultrasonic Guided Waves

Being able to monitor material characteristics non-destructively is crucial for the safe operation of aerospace structures and their optimal repurposing after the end of service. Ultrasonic guided waves are elastic waves that propagate in an elongated structure while guided by its boundaries. As guided waves are capable of propagating through large areas, they can be utilised as an efficient non-destructive method for characterising elastic and viscoelastic properties of structures. This is essentially achieved by interpreting material properties from experimental guided wave measurements, through analytical/numerical modelling of guided wave propagation. The effectiveness of the inverse material characterization is constrained by the accuracy of analytical/numerical models and the capability of experimental techniques, especially for anisotropic fibre-reinforced polymer composite materials with large design freedom. Manufacturing defects and in-service material degradation can introduce further anisotropy and inhomogeneity in material properties (e.g., non-uniformly distributed voids and cracks, varying moisture absorption through the thickness). In those situations, the simulation models with material symmetry and homogeneity assumptions may no longer match the experimental observation. To solve this problem, we need a better understanding of the wave propagation behaviour that leads to improved experimental and numerical methods.

Deadline : 15 November 2023 

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(10) PhD Degree – Fully Funded

PhD position summary/title: PhD Position Formal Methods in Computer Science

We consider human interaction to be essential for achieving reliability of deployed AI decision makers. This project will focus on modeling real-time behavior of AI systems and incorporating human feedback for guaranteeing real-time safety. This project is for you if your background is in mathematical/logic-based/formal methods and algorithms, you have strong programming skills, you have interdisciplinary mindset and are passionate about advancing state-fo-the-art in AI safety. You will conduct both theoretical and empirical research on the intersection of logic-based methods for verification under uncertainty and machine learning for interpretability. You will be part of the Sequential Decision Making Group in the Department of Intelligent Systems of the Faculty of Electrical Engineering, Mathematics and Computer Science. As a PhD researcher, you will reinforce and extend the group’s expertise in formal methods for AI safety. You will work in a dynamic and diverse environment of other PhD and postdoc researchers excited about making theoretical and algorithmic contributions in sequential decision making.

Deadline : 13 November 2023

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(11) PhD Degree – Fully Funded

PhD position summary/title: PhD Position AI-Infused Algorithms for Smart Optimization in Shared Mobility Systems

Our goal is to elevate the sustainability and efficiency of shared mobility systems. We are looking for a PhD candidate to join our team and contribute to an innovative research project with the potential to revolutionize urban transportation.

Deadline :  12 November 2023

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(12) PhD Degree – Fully Funded

PhD position summary/title: PhD Position Frontal Polymerization for Rapid and Energy-efficient Processing of Composites

This research aims at exploring novel strategies to enable the manufacturability of fiber reinforced composites thtough locally initiated frontal polymerization. Feasibility of frontal polymerization has been demonstrated for several distinct material systems, yet this has been largely limited to the manufacture of lab scale plates at controlled environments. The challenge in scaling the frontal polymerization lies in maintaining the fine thermal equilibrium between the exothermic reaction and the heat losses in the presence of geometrical and/or material variations. PhD candidate will work towards addressing these challenges through 1- developing experimental concepts to monitor and control the front propagation in molds with different features, 2- characterizing the manufacured composites to quantify the part quality and the influence of rapid thermal variations, 3- establishing the structure-process-property link to (re-)define the processing window for advanced composites. PhD candidate will be embedded in a group with expertise in composites manufacturing, will collaborate with international partners, and interact with academic and industrial partners of NXTGEN HIGHTECH Composite 01 consortium. This research contributes to the consortium by developing sustainable manufacturing routes for composites of thermosetting polymers.

Deadline : 11 November 2023

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(13) PhD Degree – Fully Funded

PhD position summary/title: PhD Position in Phase field models for hybrid composite interfaces

The interfaces between thermosets and thermoplastics can lead to interpenetrating morphologies, like spinodal decomposition. Such an interphase formation is driven by thermoset monomer diffusion followed by reaction-induced phase separation. However, the detailed understanding of the underlying physics governing the formation of such interfaces and their resulting mechanical properties remains an open challenge.

Deadline : November 10th, 2023.

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(14) PhD Degree – Fully Funded

PhD position summary/title: PhD Position in Sustainable investment in Real Estate

The modern real estate investment landscape is at a turning point, as sustainability metrics and evolving regulations becomes more important. Nevertheless, current assessment methods often remain elementary, focusing on straightforward environmental and economic metrics. Your research will fill the critical gap between traditional assessment and new needs by creating an innovative game-theoretic framework for multi-actor and multi-criteria evaluation of sustainability measures in commercial real estate investments.

Deadline : 10 November 2023

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(15) PhD Degree – Fully Funded

PhD position summary/title: MSCA Doctoral candidate New thermomagnetic materials for energy harvesting applications

HEAT4ENERGY is a 4-years EU-Horizon Europe Marie Skłodowska-Curie Doctoral Network project. The main goal of HEAT4ENERGY is to train a new generation of enablers for the European Energy Transition with the skills needed to assess the potential of new energy technologies. They will acquire these in practice by addressing the challenge of making the first realistic and energy efficient thermomagnetic energy converters for low grade waste heat (<100°C) to electricity and they will learn how to upscale these and bring them to a viable market level. The project offers training for science and technology for energy transition and climate action, as well as transferable and complementary skills and Open Science related training. Through secondments the HEAT4ENERGY PhD students will engage in all fields of materials development for energy, ranging from physics to industrial praxis.

Deadline : November 7th, 2023 

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(16) PhD Degree – Fully Funded

PhD position summary/title: PhD Position Accelerating Molecular Simulations with AI for The Design of New PFAS Absorbants

Per/polyfluorinated alkyl substances, commonly known as PFAS, are a diverse class of environmentally persistent, toxic, and carcinogenic trace pollutants in drinking water sources. PFAS and other harmful micropollutants are products of domestic and industrial activities. The concentration of such pollutants in surface water, ground water and drinking water, even at trace levels, poses a threat to the environment and public health. The new European Drinking Water Directive calls for urgent measures to remove PFAS (and other harmful pollutants) from drinking water because the traditional drinking water treatment technologies may not be able to comply with the new PFAS standards. This opening is part of the NWO funded project SYROP which focuses on the tailored design and upscaled production of fully sustainable CD polymers (CDP) to selectively target emerging micropollutants, such as PFAS. This PhD position will focus on accelerating the in-silico design and testing of CDP adsorbents using Artificial Intelligence combined with molecular simulation. In particular, Deep Learning methods based on Graph Neural Networks seem particularly suitable to handle the graph-structured data typical of molecules and molecular simulations. These advanced computational techniques will facilitate a more efficient and accurate design process for CDP adsorbents. The candidate will collaborate with a team of experts in machine learning, molecular thermodynamics, and adsorption/filtration experiments. A strong interaction with the related industry and water utilities is also expected.

Deadline : 5 November 2023 

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(17) PhD Degree – Fully Funded

PhD position summary/title: PhD Position Data-Driven Techniques for Energy-Efficient and Safe Control of Hydrogen-based Vessels

With the escalation of global energy demands and growing concerns about climate change, the pursuit of renewable and efficient energy solutions for the shipping industry is intensifying. Among the potential solutions, hydrogen fuel cells have emerged as a promising power source for various vessels, including ships and submarines. However, a barrier to their broader adoption lies in the challenges of achieving energy-efficient operation and safety. Central to overcoming these challenges is developing control algorithms tailored to the dynamics of hydrogen propulsion systems. Many algorithms have their roots in conventional fuel system designs, necessitating refinements to make them fully compatible with hydrogen-based systems. Model Predictive Control (MPC) is an advanced control approach that has garnered attention in this context. Its capability to foresee future behaviors and consider system constraints renders it especially useful to hydrogen propulsion systems. This foresight aids in proactive decision-making, ensuring the system’s safety and efficiency. Nevertheless, given the complex nature of hydrogen propulsion systems, significant concerns with MPC are related to the models’ accuracy and computational intensity. However, real-time optimization, especially over extended time horizons, is often computationally taxing. Artificial Intelligence (AI) and Machine Learning (ML) offer avenues to address these challenges. These techniques can be used to model complex systems and learn from data. Their ability to manage system complexities without explicit modeling makes them invaluable for different systems like hydrogen propulsion. However, there are inherent challenges. First, the nascent state of hydrogen propulsion means there’s limited real operational data available for ML training. Secondly, Deep learning models, in particular, can lack transparency in their decision processes, which raises concerns for safety-critical applications. To overcome those challenges, this project will focus on developing MPC algorithms for hydrogen-based vessels. To address challenges like model accuracy and computational intensity, you will use ML to develop surrogate models to approximate complex system dynamics, thereby reducing the computational burden of MPC. To ensure that AI/ML models, especially deep learning ones, are interpretable and transparent in their decision-making processes, a focus will be placed on enhancing algorithm interpretability.

Deadline :5 November 2023

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(18) PhD Degree – Fully Funded

PhD position summary/title: PhD position in Design for Strengthening Products’ Emotional Value in a Circular Economy

This particular PhD vacancy focuses on strengthening the emotional value in products during ownership. When consumers experience strong emotional value with their owned products, they are less inclined to replace them with new products. In this PhD research, the candidate will investigate how to design and develop for such emotional value, and thereby lengthen the product’s lifetime. A possible research question to answer is: How can designers design products that will trigger feelings of pride due to the fact that the owner has used them for an extended period of time? Specific attention will be paid to the opportunities provided by emerging technologies (e.g., AI, Internet of Things).

Deadline :5/11/2023 

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(19) PhD Degree – Fully Funded

PhD position summary/title: PhD position in Design for Strengthening Products’ Newness Value in a Circular Economy

This particular PhD vacancy focuses on strengthening the newness (a.k.a epistemic value) value in products during ownership. Nowadays, consumers are often attracted to new products in the market because these offer novel features. One question that the PhD candidate will contribute to answer in this PhD research is whether it is possible for products to continue providing such novel and surprising experiences during ownership. Thus how can designers design and develop electronic products that maintain their newness value during ownership. And how can emerging technologies (e.g., AI, Internet of Things) provide designers with new opportunities to trigger newness value in products due to which consumers perceive these products as valuable for a longer period of time.

Deadline : 5/11/2023

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(20) PhD Degree – Fully Funded

PhD position summary/title: PhD position in Prolonging Consumers’ Expected Lifetime of Products in a Circular Economy

This particular PhD vacancy focuses on prologing consumers’ expected lifetime of a product, either by interventions before purchase or during use. Consumers generally form expectations about the lifetime of products, for example based on their price, brand, business model and category. Via mental accounting, products slowly loose their value over time. After the expected lifetime of a product has passed, consumers feel that the product had made its money worth and there is no remaining mental book value. This will increase consumers’ likelihood to replace the product with another product. This research investigates how consumers develop these lifetime expectations and what (marketing) interventions can help to prolong lifetime expectations. Specific attention will be paid to the opportunities provided by emerging technologies (e.g., AI, Internet of Things).

Deadline : 5/11/2023

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(21) PhD Degree – Fully Funded

PhD position summary/title: PhD Position in Self-learning for Agile Navigation of Evolved Drone Bodies

Drones find more and more applications, with examples ranging from making selfies to delivering packages or localizing gas leaks. Although the requirements for these various applications are very different, they are typically tackled with a very limited set of drone types. In fact, in most cases quadrotors are used – the well-known drones with four propellers. In contrast, in nature each flying animal has a body that is highly adapted to its ecological niche. This results in huge differences in terms of both body and brain, e.g., consider the differences between eagles, bats and mosquitoes. In the SPEAR project, we aim to co-evolve the bodies and brains of drones for performing autonomous agile flight in dense, cluttered environments. TU Delft’s role in the project is to develop learning mechanisms that allow the evolutionary process to determine the fitness of different drone embodiments. The learning algorithms have to be flexible enough to deal with the various drone bodies and sensor configurations that arise during the artificial evolution, while being sample efficient enough to allow quick evaluation and robust enough to enable transfer from simulation to reality. We are looking for a PhD candidate that will develop together with us the algorithms for learning a navigation policy for the different body and sensor configurations that arise during artificial evolution. In the quest for efficient, flexible, and robust learning mechanisms, we will consider learning paradigms ranging from reinforcement to unsupervised and self-supervised learning.

Deadline :  5 November 2023 

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(22) PhD Degree – Fully Funded

PhD position summary/title: PhD Position in Urban Climate Morphology

We invite applicants to join our international and interdisciplinary team of passionate and ambitious academic professionals at the Faculty of Architecture and the Built Environment, TU Delft. One of the world’s leading institutes in the field, ranked 3rd in the QS World University by Subject-Ranking. We are looking for a motivated person to strengthen our team at the Section Environmental Technology and Design (ETD), Department of Urbanism, in the 4 TU Project HERITAGE. HERITAGE (HEat Robustness In relation To AGEing cities) is a research program aiming at the detection, reduction and prevention of heat-stress occurring due to the ageing of built environmental settings and buildings in Dutch cities through socio-technical solutions. This programme is a 4TU initiative connecting the four universities of technology in The Netherlands: the Delft University of Technology, Eindhoven University of Technology, University of Twente and Wageningen University and Research. The TU Delft team of HERITAGE is seeking a highly motivated and talented candidate for an interdisciplinary PhD position focussing on the relationship between urban form and outdoor and indoor heat in the context of climate change. We specifically welcome applications from candidates with an interest and experience in conducting data-driven research and linking spatial analysis at different scales. You will be involved in HERITAGE activities related to work package 4 which aims to i) develop clustering classification methods to identify building and context types based on key form parameters that influence outdoor and indoor temperatures; ii) assess climate performance of morphological types by using climate simulations and on-site measurements

Deadline : 5 November 2023 

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(23) PhD Degree – Fully Funded

PhD position summary/title: PhD Position Researcher in Silent Ship Propellers

According to the most authoritative marine biologists, the acoustic pollution afflicting our seas and oceans represents an increasing threat for the demography of marine live. Shipping is the dominant contributor to the total acoustic pollution, with propeller cavitation generating most of the shipping noise. Cavitation is an abrupt liquid-to-gas transition that occurs if the local fluid pressure falls below a critical pressure, often assumed to be the vapour pressure. At Delft, a unique facility has been recently built with the aim of studying propeller cavitation and its noise emission for first time in non-fresh water. This research project focuses specifically on the initiation of cavitation, or cavitation inception. Cavitation inception necessitates not only of a particular condition of low-pressure, but also of an activation nucleus, which is typically a microbubble. While it is well-known that cavitation inception highly depends on the size distribution and on the concentration of the incoming nuclei, the precise effects of dissolved salt and surfactants in this process remain elusive. The project aims precisely at investigating cavitation inception in non-fresh water. Experiments will be conducted in the new Multi-Phase Flow Tunnel (MPFT) where state-of-the art techniques, such as array of hydrophones and time-resolved flow visualizations will be applied to shed some light into the physics of this complex multi-phase flow problem. The experiments will be complemented with numerical simulations of point-bubbles and finite-size bubbles (based on a so-called front-tracking method), allowing us to better quantify and understand the effects of free nuclei on tip vortex cavitation.

Deadline :  5 November 2023 

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(24) PhD Degree – Fully Funded

PhD position summary/title: PhD Position Optimal Control of Multi-function Radar Tasks

Radar is still an unrivaled sensor in air traffic control, navigation, military applications, and environmental monitoring, like weather and sea surface. Modern phased array radar systems have the ability to adapt the direction and shape of their beams, and change their waveforms almost instantaneously within a wide range of possibilities.This has lead to the rise of multi-function radar (MFR) which can execute a large range of sensing tasks seemingly in parallel. To a large degree the control of these tasks, such that the radar performance fully satisfies the user, is still an open problem. We are looking for a PhD-student that will be able to apply optimal detection/estimation, control and information theory to develop new solutions for this problem. Important aspects are relatively easy interpretation of the optimal solution by non-experts and notion of guarantees for the performance. You will primarily work with Associate Professor Dr. Hans Driessen, and you will have the opportunity to formulate and guide Master projects. 

Deadline : 2 November 2023

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(25) PhD Degree – Fully Funded

PhD position summary/title: PhD Position Computational Design of Adsorbents for the Removal of Micropollutants from Water

Per/polyfluorinated alkyl substances, commonly known as PFAS, are a diverse class of environmentally persistent, toxic, and carcinogenic trace pollutants in drinking water. PFAS and other harmful micropollutants are products of domestic, industrial, and agricultural activities. The concentration of such pollutants in drinking water, even at trace levels, poses a threat to the environment and public health. New European Directives call for urgent measures to remove PFAS (and other harmful pollutants) from drinking water because the traditional drinking water treatment technologies may not be able to comply with the new standards.  This opening is part of the NWO funded project SYROP which focuses on the tailored design and upscaled production of fully sustainable CD polymers (CDP) that can selectively target emerging micropollutants, such as PFAS. This PhD position will focus on the in silico design and testing of CDP adsorbents mainly using advanced molecular simulation methods. The candidate will collaborate with a team of experts in machine learning and adsorption/filtration experiments. A strong interaction with the related industry and water authorities is also expected.  

Deadline : 1 November 2023

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(26) PhD Degree – Fully Funded

PhD position summary/title: PhD Position Mathematical Analysis of PDEs

The Delft Institute for Applied Mathematics (DIAM) at Delft University of Technology (TU Delft) invites applications for a full-time PhD position in the field of mathematical analysis of partial differential equations (PDEs). The PhD candidate will be supervised by Dr. H. Yoldaş. The research topics primarily revolves around PDEs stemming from structured population dynamics and kinetic theory. Examples of such equations include growth-fragmentation equations, run and tumble equations that model bacterial movement, and classical kinetic equations in gas theory, including the Boltzmann equation. For more insights into the research, please visit the Research & Publications of Dr. H. Yoldaş’s webpage. As a PhD student in this position, you will investigate the qualitative and quantitative aspects of these PDEs employing a diverse range of mathematical tools from PDE theory, functional analysis and probability theory. The specific research direction will be tailored to your background and interests, potentially encompassing topics such as well-posedness, long-term behavior analysis of PDEs, deriving PDEs from particle systems, and numerical analysis.

Deadline : 1 November 2023

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(27) PhD Degree – Fully Funded

PhD position summary/title: PhD 3D digital imaging metrology of semiconductor devices

Integrated circuits are the cornerstone of our digital society. The practical use of integrated circuits depends strongly on low-cost, high-quality assembly, packaging, processing, inspection, and connection of chips into electrical components. Control of these processes is done with high-performance optical imaging. When imaging at high resolution, wavefront aberrations deteriorate the image quality of these tools. Since these aberrations lead to a loss of accuracy in the alignment of the semiconductor devices it is of paramount importance to mitigate them. High resolution imaging over a large field of view will be implemented for digital holography. When imaging the wavefield over a large field of view the aberrations are spatially dependent. Therefore they require advanced non-isoplanatic correction techniques. The PhD candidate will model non-isoplanatic aberrations in optical systems and develop computational techniques to compensate for them. These techniques will then be implemented in a lab-based experimental digital holography setup. The performance of the developed techniques will be quantified and optimized both in terms of obtained spatial resolution as well as in computational times. Moreover, since these aberrations have to be corrected in an industrial-type high-throughput environment the computations for removing them have to be performed at high speed.

Deadline :31st of October 2023 

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(28) PhD Degree – Fully Funded

PhD position summary/title: PhD position – Biomolecules as antennas

Transduction is happening everywhere around us. In biology, cells transduce signals from one cell to another, from the outside environment to the inside, and across different cellular compartments. An example of transduction in engineering is the use of microphones, which convert sound waves into electrical signals via intermediate mechanical oscillations. However, there is currently a lack of technology to emit and detect in the THz range of the electromagnetic spectrum; a problem also known as the ’THz gap’. To achieve broad band quantum transduction, particularly in the THz range, one solution is to use biomolecules. Biomolecules are known to absorb in that range, and they have the potential of being modified and tuned, therefore making them ideal THz bioantennas. This PhD project will explore and develop THz biomolecular antennas, possibly with making use of lipid bilayers, and then interface such bioantennas with quantum materials.

Deadline : 31-10-2023

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(29) PhD Degree – Fully Funded

PhD position summary/title: PhD Position in Aircraft Maintenance Planning and Optimisation

Aircraft maintenance planning is a complex task that spans very large timelines and requires high flexibility. In the current industry landscape, separate entities and departments often perform maintenance planning with their own tools and methods. This siloed approach makes it difficult to perform maintenance planning holistically, which can lead to suboptimal solutions. This PhD position offers you the opportunity to significantly impact the field of aircraft maintenance planning. You will work with a team of experienced researchers to develop a framework for connecting different parts of the maintenance planning process. This framework will enable integrated decision-making that can improve the efficiency and effectiveness of aircraft maintenance planning.

Deadline :31 October 2023

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(30) PhD Degree – Fully Funded

PhD position summary/title: PhD Position in Mechanical Engineering

Touch interfaces are replacing physical buttons, dials, and switches in the new generation of cars, aircraft, and vessels. However, vehicle vibrations perturb finger movements and cause erroneous touchscreen inputs by users. Furthermore, unlike physical buttons, touchscreens cannot be operated by touch alone and always require users’ visual focus. Hence, despite their numerous benefits, touchscreens are not inherently suited for use in vehicles, which results in an increased risk of accidents. Haptic Interface Technology Lab (HITLab) is looking for a highly motivated Ph.D. student to address these problems by developing novel in-vehicle touchscreens that simulate physical touch interactions with artificial tactile feedback adapted to vehicle perturbations, user differences, and variations in finger contact dynamics.

Deadline :  31 October 2023

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(31) PhD Degree – Fully Funded

PhD position summary/title: PhD Position: WadSED: Wadden Sea and Estuaries: System Dynamics and Sediment Management under Climate Change

We are seeking a PhD candidate for WadSED, a large NWO project on the Wadden Sea tidal basins and the Western Scheldt estuary. We seek to understand these systems and predict their morphodynamics and long-term morphological development under influence of natural dynamics, human interference and sea-level rise. Multiple PhDs and Postdocs will work on this challens and link new insights to the societal challenges regarding sediment management, ecology and flood safety. The topic of this PhD project focusses on the respons of the Wadden Sea and other tidal basins to accelerating sea level rise.

Deadline : 31 October 2023

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(32) PhD Degree – Fully Funded

PhD position summary/title: PhD Position in New Meanings of Value in the Built Environment

We are looking for a doctoral candidate for a fully funded position (0.8 – 1.0 FTE) to conduct individual research focused on understanding new meanings of value in the built environment. In commercial property markets the focus is placed on assessing real estate as an investment asset. This type of thinking leads to discussions in industry and studies in academia centered around the identification of market and investment values, return and risk assessments, or financial portfolio management. Research demonstrates that this type of thinking limits investments that could yield in energy efficiency in buildings or broader sustainable and circular actions in real estate. Expenditures supporting these actions are often seen as affecting investment returns, creating tangible and hidden costs and risks to the investor and therefore lacking a compelling business case. Traditional appraisal methods are too focused on determining property values based on physical building-specific features (e.g., size of the building, energy consumption, or number of rooms), taking a perspective of an exchange value of a building but ignoring intangible aspects that can create a different value for the customer. As value is traditionally understood as a monetary expression of customers’ willingness to pay, those intangible aspects of a tangible asset (building) should also be included in pricing and appraisal. Therefore, a value-based pricing approach could be a potential way to encourage commercial real estate markets to move away from cost and competition to more value-based logics. A value-based approach is common for service and customer-experience driven economies where value is understood as subjective (and changing) to the user/buyer. However, this type of approach requires a different view to what we define as value and/or different approach to the overall valuation process. With this research, we aim at creating a framework that could serve as a tool for further development of new valuation/appraisal approaches for commercial real estate. To reach this, multiple steps need to be taken:

Deadline : 29 October 2023 

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(33) PhD Degree – Fully Funded

PhD position summary/title: PhD Position in Quantum Networks

The vision of a Quantum Internet is to provide fundamentally new internet technology by enabling quantum communication between any two points on earth. Such a Quantum Internet will – in synergy with the ‘classical’ Internet that we have today – connect quantum processors in order to achieve unparalleled capabilities that are provably impossible using only classical communication.

Deadline : Open until filled

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(34) PhD Degree – Fully Funded 

PhD position summary/title: PhD Position Power System Defense Against Cascading Failures

The eFORT project, funded under Horizon Europe, is recruiting a talented, enthusiastic, and ambitious candidate to perform excellent research and achieve breakthroughs in the field of power system resilience. The main objective of eFORT is to make interconnected power grids more resilient and reliable to failures, cyber attacks, physical disturbances, and data privacy issues. To this end, a set of technological innovations will be developed for the detection, prevention and mitigation of risks and vulnerabilities with positive impacts on power system operation and stability. The eFORT solutions will be demonstrated at TSO, DSO, digital substation, and microgrid levels in 4 real demonstration environments. Within the eFORT project, Delft University of Technology (TU Delft) is hiring a doctoral candidate on the subject: “Power system defense against cascading failures.” You will conduct research on power system stability and develop self-healing capabilities for interconnected power grids to stop the propagation of cascading effects and prevent a blackout. First, a computational method will be developed, which uses a digital twin of the power grid to assess in real-time the impact of cyber attacks on power system operation and analyse how they can initiate cascading failures that may lead to a blackout. Then, you will develop a method using AI to enable self-healing grid capabilities and stop propagation of cascading outages. This will be achieved by a novel system for coordinated, self-healing emergency controls using wide area monitoring, protection and control (WAMPAC) and artificial intelligence. Our large RTDS infrastructure coupled with a digital substation and Control Room of the Future (CRoF) will be used to conduct research and demonstration of prototype solutions.

Deadline : 29 October 2023

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(35) PhD Degree – Fully Funded

PhD position summary/title: PhD Position AI Ethics in Military Contexts

As in many other societal areas, AI systems are developed for and deployed in military contexts. These developments can have enormous consequences for potential combatants, civilians, and society at large. The application of AI systems in military contexts involves high moral stakes, including life and death decisions. It is, therefore, crucial to develop and deploy AI systems responsibly in this area. Ethical reflection and deliberation can be integrated in processes of development, design and deployment, so that relevant moral and societal values (e.g. meaningful human control, safety, reliability, accountability and explainability) are supported and promoted.

Deadline :  27 October 2023

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(36) PhD Degree – Fully Funded

PhD position summary/title: Phd Position Deep Learning Propeller Design Optimization

Underwater radiated noise has long been recognised as a substantial environmental concern, leading to the adoption of increasingly stringent guidelines on minimising it . Propeller cavitation is the main source of shipping noise and hence presents a clear priority in achieving satisfactory design trade-offs between efficiency and induced noise pollution. However, accurate prediction of cavitation at early design stages is severely limited by the cost of experimental studies or high-fidelity numerical simulations. To provide better support to propeller designers it is necessary to employ novel numerical methods capable of accurate predictions of the transient complex cavitating flows with minimal computational effort. Design-by-optimization methodologies can be used to design efficient propellers that excel in various operating conditions while addressing noise and vibration concerns. However, one challenge is that the computational requirements for this optimization procedure can be time-consuming, as it may involve defining and verifying thousands of different geometries using high-fidelity numerical methods. This further limits the use of presently-available numerical tools in such optimisation frameworks. To address this challenge, present research proposes the utilization of Deep Learning techniques. Specifically, you will develop a novel application of a Deep Learning architecture, known as Graph-U-Net, leveraging U-Net encoder-decoders to predict flow field dynamics and model turbulence. One key innovation lies in the generalization of these networks to accommodate unstructured geometries, such as a propeller mesh, using techniques derived from Graph Neural Networks and Algebraic-Multi-Grid methods. This approach has the potential to substantially reduce computational requirements, thereby enabling a design-by-optimization approach. Another innovative aspect of this research will be the development of a physics-informed Machine-Learning framework. This framework will aim to improve the speed, generalization, and interpretability of the optimization process by leveraging a high-speed Boundary Element Method with global irrotational flow physics that will be supplied to the graph and coupled with a recursive transformer structure for ensuring consistent dynamics. The research will be carried out in close cooperation with the Maritime Research Institute Netherlands (MARIN), working on the Marie Skłodowska-Curie Actions funded project SCALE.

Deadline : 27 October 2023 

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(37) PhD Degree – Fully Funded

PhD position summary/title: PhD Position Particle Free Contactless Wafer Handling, WP1

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 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 in collaboration with our industrial partner, VDL-ETG, an important OEM supplier in the world-leading Dutch high-tech industry, we will develop new concepts for future handling systems for Si-wafers. This system will handle Si-wafers without mechanical contact when possible, and when unavoidable, make sure that the mechanical contact is without damage or contamination.

Deadline : 24 October 2023

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(38) PhD Degree – Fully Funded

PhD position summary/title: PhD Position Particle Free Contactless Wafer Handling, WP3

Every mechanical contact between system components 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 in collaboration with our industrial partner, VDL-ETG, an important OEM supplier in the world-leading Dutch high-tech industry, we will develop new concepts for future handling systems for Si-wafers. This system will handle Si-wafers without mechanical contact when possible, and when unavoidable, make sure that the mechanical contact is without damage or contamination.

Deadline : 24 October 2023

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(39) PhD Degree – Fully Funded

PhD position summary/title: PhD Position Morphing Wind Turbine Blades for Turbulence Absoption

To meet the growing renewable energy demand, there is a need for extremely large wind turbines operating in challenging offshore areas. These giant machines experience high levels of flow unsteadiness along the rotor blades in space and time. Current wind turbines cannot sense and react to the complex local environment due to their limited degrees-of-freedom at blade- and rotor-level. This causes blade sections to operate at off-design conditions and results in undesired energy losses. Local control handles along the blade are required to resolve this issue, for which morphing wing technology is an elegant and robust solution. The potential of morphing wind turbine blades for power enhancement remains elusive due to the lack of understanding of the complex unsteady aerodynamic phenomena involved in the morphing process. The key challenge in evaluating this technology is to predict how the flow evolves around shape-shifting wings, particularly during the transition phase from one airfoil shape to another. This information is crucial to determine effective morphing control strategies. The purpose of this project is to study the unsteady aerodynamic behaviour of shape-shifting wings using a combined experimental and numerical platform, to demonstrate morphing wing technology in complex flows for power enhancement. The numerical platform will include an advanced unsteady aerodynamics module, while the experimental morphing platform, tailored specifically to wind turbine applications, will serve to gather training data and as demonstrator.

Deadline :  23 October 2023

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(40) PhD Degree – Fully Funded

PhD position summary/title: PhD Position CO2well: Coupled Well-Reservoir model for CO2 and H2 storage

Subsurface reservoirs are used for various applications driving the energy transition towards zero-carbon energy. Making optimal use of subsurface reservoirs is a great challenge for society these days. Geological CO2 and hydrogen storage can play a significant role in modern energy transition. However, accurate modeling of gas injection applications in deep subsurface reservoirs is a challenging task. First, the simulation framework should include complex physical phenomena, i.e., thermal-multiphase-chemical flow. The computational models depend on many uncertain reservoir parameters and imprecise measurements which require a significant number of forward modeling runs for performance optimization or risk assessment. For that, the modeling process should achieve the required level of accuracy, robustness, and efficiency. To satisfy these requirements, we are going to utilize the Operator-Based Linearization (OBL) and drift-flux multisegment techniques. OBL is the advanced parametrization technique allowing to translation of the governing physics of complex flow and transport processes into multi-dimensional tables representing the physical terms in governing partial differential equations. A drift-flux multi-segmented well approach allows to representation of realistic multi-phase flow in the wellbore and surface facilities which can be directly coupled with the flow in the reservoir. Using these two methods, we extend our Delft Advanced Research Terra Simulator (DARTS) to accurate modeling of well-reservoir interactions which play a significant role in the security of CO2 and H2 reservoir storage. 

Deadline : 22 October 2023

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(41) PhD Degree – Fully Funded

PhD position summary/title: PhD Position Digital Twins for Stability of Power Systems with High Penetration of Renewable Energy Sources

TwinEU project, funded under Horizon Europe, is recruiting talented, enthusiastic, and ambitious PhD candidates to perform excellent research and achieve breakthroughs in the field of digital twins for power systems. The strategic goal of TwinEU is to leverage a unique set of competences from grid and market operators, technology providers and research centres to create a concept of Pan-European digital twin based on the federation of local digital twins and enable a reliable, resilient, and safe operation of the infrastructure while facilitating new business models that will accelerate the deployment of renewable energy sources in Europe. Within the TwinEU project, TU Delft is hiring a doctoral candidate on the subject: “Digital twins for stability of power systems with high penetration of renewable energy sources”. As a PhD candidate, you will conduct research into and develop new principles and methods to enable effective utilization of the grid-edge data of TSO and DSO networks in order to properly dealt with random fluctuating generation/consumption/failures affecting the security of supply in intra-hour/sub-minute/sub-second time frames.

Deadline : 22 October 2023 

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(42) PhD Degree – Fully Funded

PhD position summary/title: PhD Position Flight Control Law Design for a Flying Wing Aircraft Concept

A disruptive technology proposed by TU Delft in order to make commercial airplanes more efficient and reduce their emissions is the Flying-V concept aircraft. The Flying-V is a long-haul aircraft where the passenger cabin, the cargo hold and the fuel are all located in the wing. This means significantly less drag and also less weight which both result in an impressive 20% reduction in fuel consumption with respect to tube-and-wing aircraft. The innovative aerodynamic design does not involve neither a horizontal tail plane nor a vertical stabilizer. Pitch and roll control are provided by segmented elevons at the trailing edge of the highly swept wing whereas yaw control is provided by rudders integrated in the winglets. This architecture raises a number of challenges concerning the flight control system both for the pitch and yaw channels, namely very nonlinear aerodynamics for high angle-of-attack and reduced lateral-directional stability. This PhD position focuses on the design, testing and validation of a full flight control system for the Flying-V, aiming to improve its handling qualities and provide with robustness against varying flight conditions, model (aerodynamic and mechanical) uncertainties and external disturbances such as wind gusts. To this end, high-fidelity aerodynamic models issues from various sources will be used and the handling qualities will be validated using TU Delft SIMONA research simulator and also through flight tests of the aircraft prototype.

Deadline : 22 October 2023

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(43) PhD Degree – Fully Funded

PhD position summary/title: PhD Position in Large Eddy Simulations for Hydrogen Flames

A PhD position has become available at the Faculty of Aerospace Engineering of TU Delft, department of Flow, Propulsion and Technology (FPT), Chair of Sustainable Aircraft Propulsion (SAP), within the context of development of hydrogen-based technologies for combustion systems. We are racing against time to find a clean, yet abundant, energy source able to arrest global warming. Hydrogen has all the characteristics to address this challenge: it can be produced cleanly from water; it is incredibly energetic; and more importantly, it is carbon-free. However, hydrogen’s strong reactivity and diffusivity make the control of its flame in energy-generation devices extremely challenging. Moreover, toxic nitric oxides (NOx), a major concern for air quality, are still abundantly produced in a hydrogen flame. Enabling the use of hydrogen requires thus solutions where the flame is stable and with ultra-low NOx at the same time, and at any power setting. The present research will explore innovative methods to suppress NOx and keep the flame stable at the same time, such as water injection, intensive strain, cold injection and magnetic conditioning. The assessment and control of the flame dynamic response under different conditions and combustion regimes is an integral aspect of this research. The flame dynamics will be fully characterised by using high-fidelity large eddy simulations and theoretical analyses. The final goal is to achieve control of the hydrogen flame at any power setting, so to pave the way for the exploitation of green energy.

Deadline : 20 Octotober 2023 

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(44) PhD Degree – Fully Funded

PhD position summary/title: 2 PhD Positions on Secure and Optimal Control of Renewable Energy Systems

Renewable energy sources, such as wind farms, are crucial for realizing climate neutrality, energy independence, and energy security. With their increased penetration in the electricity grid, there is a strong need for distributed control algorithms that can optimally determine the amount of energy to produce, and its destination (e.g., grid, storage, hydrogen). Still, the issues of scalability (think about wind farms comprising hundreds to thousands of turbines), resiliency to failures, uncertainty in the environment and the market, and security against malicious actors are far from be solved.

Deadline :20 October 2023 

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(45) PhD Degree – Fully Funded

PhD position summary/title: PhD Position Adaptive Optimization and Learning Methods for Transportation Systems

One of the biggest challenges for transportation systems is how to wisely utilize the available resources while responding to the demand. According to Eurostat, 20% of road freight kilometres in the EU in 2020 were driven by empty vehicles and this is similar for other modes of transportation. There are various reasons for the underutilization of transportation capacity. Firstly, there are uncertainties in the system, e.g., demand is fluctuating, the travel and service times vary significantly. In order to cope with that, operators frequently end up allocating more resources than needed. Secondly, transport systems have complex supply-demand interactions which makes it difficult to optimize the decisions on different resources. Underutilization of capacity entails costs that do not generate revenue and contribute to CO2-emissions, whereas the transportation sector is striving for sustainability goals. Being able to adapt the decisions – e.g., the network design, allocation of capacity, routing and scheduling – according to evolving demand and conditions in the transport network is a promising direction to improve the utilization of available resources. ADAPT-OR project is funded by European Research Commission for Fundamental Research. The aim of ADAPT-OR is to develop self-learning capabilities towards adaptive transportation systems by leveraging the intersection of operations research, behavioural modelling and machine learning methodologies. The idea is to make use of information from the system itself across different decision-making levels, from the users and from the external environment in a self-learning manner in order to continuously adapt the decisions at different levels. For example, with a continuous input from the operational level on the delays in different parts of the network, the fleet allocation can be adapted at the tactical level. Similarly, depending on the trends in behavior for a given delivery service, the network design can be adapted.

Deadline : 20 October 2023

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(46) PhD Degree – Fully Funded

PhD position summary/title: PhD Position Data-driven and modular control for energy and agricultural systems

Over the last century automation has produced unprecedented improvements in productivity, profitability, and safety for a variety of industrial processes. Theses gains, however, are largely concentrated in centralized applications (e.g., chemical plants) or in mass-produced products (e.g., automobiles), in which economies of scale afford for a team of specialists to develop and implement these model-based automation strategies. In many energy and agricultural applications, such as heating ventilation and air-conditioning (HVAC), micro-grids, and controlled environment agriculture (CEA), the cost of deploying these advanced controller design strategies for a single facility often exceeds their potential economic benefit. For these applications, we instead require control algorithms that are data-driven and modular in that we can apply the same low-cost approach to deploy controllers for multiple systems with similar physics, but different parameters.  In this PhD project, you will design data-driven and modular controller algorithms to address these limitations in energy and agricultural systems. The goal is to leverage first-principles knowledge as well as advances in machine learning, optimization, and data-driven control to design a streamlined controller synthesis procedure. In particular, we will focus on modular approaches that allow us to apply the same low-cost controller synthesis procedure to deploy controllers for a variety of systems with similar physics, but different parameters. By reducing the cost to deploy these advanced automation and control algorithms, we can expand the scope of these advanced automation strategies and thereby bring significant improvements to the resilience, economics, and sustainability of modern energy and agricultural systems. 

Deadline : 20 October 2023 

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(47) PhD Degree – Fully Funded

PhD position summary/title: PhD Position Deep Meta-learning on Learning Curves to Improve Machine Learning

We are looking for a critical and open-minded person to come work with us to deepen our understanding of learning curves. Learning curves in machine learning plot performance versus training set size. By extrapolating learning curves we can predict how many training samples are necessary for a particular performance. Learning curves can also be used to speed up learning algorithms, model selection, and hyperparameter tuning. However, unexpected and strange learning curves make this task difficult. Furthermore, there is much uncertainty about the general shape of learning curves: are they exponential, power law, or do they have other predictable shapes? A better understanding of learning curves can provide deeper insights into how learning algorithms work, and  may inspire new machine learning theory and improved learning algorithms. You will analyze learning curves, develop deeper knowledge about their shape, and exploit that knowledge to improve applications that rely on learning curves (hyperparameter tuning, model selection, predicting the amount of data needed). The plan in this project is to develop meta-learning algorithms for learning about learning curves. By analyzing a database composed of a large number of learning curves, we want to extract data-driven insights and exploit them. We see especially potential for deep learning methods (meta-learning and generative modeling), but there is room to explore and develop other kinds of models. Moreover, we are interested in translating findings into human-understandable insights (explainable AI) which can inspire new theoretical insights and new algorithms.

Deadline : 20 October 2023

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(48) PhD Degree – Fully Funded

PhD position summary/title: PhD Position Discovering the Mechanisms that Cause Bicycle Crashes Through The Merger of Video and Computational Modeling     

We are seeking a PhD researcher for a 4-year contract to develop simulation and data analysis tools that lead us to understanding the mechanisms at play in bicycle and other two-wheeler crashes. You will work primarily with video data and computational neuromuscular models to develop the next generation of predictive algorithms that connect human control to bicycle crash dynamics. Your project is partially supported by the OpenSim Creator: Empowering Biomedical Research with Biomechanical Models grant funded by the Chan-Zuckerberg Foundation’s “Essential Open Source Software for Science” program. The software developed during the research will be contributed to OpenSim as open source software and the research results will be submitted to appropriate scientific journals and conferences. You will simultaneously work as a member of the Computational Biomechanics Lab and the Bicycle Lab both part of the BioMechanical Engineering Department. You will be supervised by Dr. Jason K. Moore and Dr. Ajay Seth. You will work BSc, MSc, and PhD candidates in the involved research groups from the BioMechanical Engineering Department and other departments in the university.

Deadline : 20 October 2023 

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(49) PhD Degree – Fully Funded

PhD position summary/title: PhD Position Developing Acoustic Metamaterials for Sound Absorption

The main objective of this research is to overcome the limitations of conventional sound-absorbing porous foams by exploring innovative solutions. Rather than relying on traditional stochastic structures of porous foams, the proposed approach involves utilizing periodic porous structures combined with metamaterial designs. Firstly, we will investigate periodic porous structures based on TPMS (triply periodic minimal surface) in terms of its acoustic modelling methodologies and sound absorption properties. Subsequently, the next phase of our research will center on the exploration of  methodologies aimed at further enhancing the sound-absorbing properties of these structures, particularly in terms of broadband and/or low-frequency absorption capabilities. To accomplish this, we will employ various techniques, including the implementation of porosity gradation and the incorporation of rigid scatters or resonators, ultimately leading to the development of metamaterial structures.

Deadline : 17 October 2023 

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(50) PhD Degree – Fully Funded

PhD position summary/title: PhD Position in Testing and Analysis of Distributed Systems

The PhD project aims to develop software testing and analysis techniques for discovering vulnerabilities in distributed systems and blockchains. Almost all software systems we use today are distributed, and our computations increasingly rely on distributed systems and blockchains. However, these systems are difficult to design and implement correctly. They may fail to ensure correctness in executions with unforeseen interactions of concurrent events, network, and process faults. It is critical to detect and diagnose such executions for the reliability of these systems. The PhD project will bring together distributed systems knowledge with software engineering to develop efficient testing methods for distributed systems and blockchains. We envision the project to have both scientific impact on developing new software analysis methods and practical impact on implementing them as practical tools for analyzing distributed systems.

Deadline :  16 October 2023

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(51) PhD Degree – Fully Funded

PhD position summary/title: PhD Position Flow Control of Transitional and Separating Flows

The project aims at gaining understanding of transitional and separating flows on wings and developing novel passive or active flow control techniquesOur team is closely collaborating with the group of Professor Serhiy Yarusevych at the University of Waterloo, Canada, and opportunities for research stays are available within this project.

Deadline :  15 October 2023 

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(52) PhD Degree – Fully Funded

PhD position summary/title: PhD position in Scalability of microbial electrosynthesis from CO2

Power-to-Chemicals and Power-to-Fuels processes are the future of the chemical industry. The chemical industry is a major greenhouse gas emitter. To meet their GHG emission reduction target, the chemical industry must use alternative carbon sources (e.g. CO2) and electricity-based processes (Power-to-X). The perspective of a highly electrified chemical industry where renewable feedstocks and energy are the basic ingredients of chemicals is very appealing to many societal, industrial and governmental stakeholders. Microorganisms are able to grow on electrodes and utilize renewable electricity to convert CO2 to valuable chemicals. This Power-to-X process is called microbial electrosynthesis (MES), and is the focus of this PhD project. This PhD project is part of a larger project (2 PhD and 2 postdocs) and a consortium composed of four international companies, which all together will develop a scalable MES process that produces hexanoic acid (C6, a major base chemical) from CO2 and renewable electricity. Our distinctive approach will elucidate how to avoid rate and yield limiting steps from micrometer to meter scale, by guiding experiments and reactor/electrode design with comprehensive multiscale modelling. We will use the attained insights on the working mechanisms of MES to develop a scalable process capable of producing pure hexanoic acid from CO2 under industrially relevant conditions.

Deadline :  15 October 2023

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(53) PhD Degree – Fully Funded

PhD position summary/title: PhD Position in Soil-Structure Interaction of Flood Defences

Our faculty has been awarded a large research grant to investigate “Future flood risk management technologies for rivers and coasts” by the Dutch National Science Foundation (NWO). As part of this program we are looking for a PhD candidate for the work package focussing on soil-structure interaction in flood defences. Traditional raising and widening methods (berms) to strenghten dikes / levees are difficult to realize in densely populated areas. As alternatives, structures within a soil dike can be implemented, e.g. sheet piles, diaphragm walls or piles. It is difficult to evaluate the performance of soil structure (SS) solutions, and the best ways to model and evaluate their performance and reliability. Yet, at the same time SS solutions can enhance robustness (i.e. performance during overloading) and limit sensitivity to local geotechnical weaknesses. It is the aim of this work package to develop methods to model, assess and design soil-structure interaction and to assess the performance, robustness and reliability of structural solutions in earthen flood defences. The following tasks and activities are foreseen. Firstly, it will be investigated how various SS solutions in dikes can be modelled, using Finite Element Methods (FEM) and where possible analytical approaches. This will commence and continue from previous studies and validated with data from actual projects. A second task is the probabilistic modelling of SS solutions. This is done using probabilistic FEM frameworks for combined soil-structure systems, e.g. for quay walls which need to be further extended to flood defences. The third task  is to assess the performance of SS solutions during the various phases of the life cycle. Particularly during and after the construction phase several problems for buildings near the dike could occur, e.g. due to settlement and deformation. As a case study for the construction phase a hindcast modelling of the Lekdijk dike reinforcement will be performed. During this actual reinforcement with piles in the dike, many houses nearby were damaged. This case will be studied, also utilizing satellite monitoring on deformation of these houses. Also, the models will be applied to assess performance after construction and during the life time (usually several decades) to assess long term performance and reliability. The final task will be to develop design guidance for various SS solutions for a riverine case to characterize their reliability, durability and performance during overloading to enhance robustness.

Deadline : 15 October 2023 

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(54) PhD Degree – Fully Funded

PhD position summary/title: PhD Position Integrated Topology and Controller optimization for Mechatronic positioning systems

Motion systems are used throughout the semiconductor and high-tech industry for fast and precise positioning in e.g. microscopes and chip production process. Motion system design unites structural dynamics, actuation, sensing and control. To achieve optimal performance of these mechatronic systems, this project aims to develop new computational design approaches where structure and controller design are integrated. Use of topology optimization and additive manufacturing allows for maximum geometric design freedom, and consideration of tailored damping material distribution is expected to raise the system performance to unprecedented levels. A foundation for integrated controller-structure optimization is in place from a previous project. Your challenge is to extend this towards more realistic control solutions and performance targets, and to develop a method to co-optimize a tailored and manufacturable damping layout.  While the main focus is on development of new computational design methods for motion systems, realization and validation of two full-scale hardware demonstrators, in close collaboration with industry partners, also forms an important part of this project.  

Deadline : October 15, 2023

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(55) PhD Degree – Fully Funded

PhD position summary/title: PhD Simulation of Scanning Electron Microscope (SEM) Images

Electron microscopy is at the heart of innovations and applications on the nanoscale in a wide range of sectors. The technology is, for instance, used in the semiconductor industry to observe engineered nanodevices, as well as for medical applications like cancer research. The major challenge of using a scanning electron microscope (SEM) is to correctly interpret the images created by the tool. The formation of these images depends on how the electron beam interacts with the composition and topology of the samples. Your challenge as a PhD student at TU Delft is to further develop and program models that will enable simulation of such SEM images. You will work closely with our partner ASML, leveraging fundamental research to improve nanoscale electronic device manufacturing. To ensure truly reliable, high-resolution measurements, it is crucial to understand the physics of the image formation in a SEM. You will build on existing models (Nebula), embedding the comprehensive physics of electron beam scattering in substrates and semiconductor devices. One of the key phenomena you will focus on, is image artifacts caused by charges accumulated on sample surfaces. Having developed your models, you will translate them into software programs and conduct simulations. You will get the unique opportunity to compare experimental and simulated SEM images, using real SEMs to evaluate your models. As part of your role, you will write scientific articles and attend leading international conferences. In addition, you will mentor bachelor and master students, and supervise tutorials, exercise classes and labs.

Deadline : Open until filled

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(56) PhD Degree – Fully Funded

PhD position summary/title: PhD Position Shaping the Geometry of Uncertainty for Data-driven Control

Real-world systems are challenged by constantly increasing complexity and uncertainty. This uncertainty is often unknown and dynamically varying and may need to be described locally with sufficient detail. For instance, a hospital robot may seek to deliver blood samples while avoiding potential collisions with humans in the corridors by tuning its velocity in the safest possible way. In traditional approaches, such decisions are taken using a probability distribution of the uncertainty. This has the drawback of working with the single distribution that is assumed, which may even need to be chosen in an arbitrary way. The goal of this PhD project is to build ambiguity sets of probability distributions that hedge against plausible variations of stochastic uncertainty models. It will optimize the geometry of these ambiguity sets to prevent their potential conservativeness and track their time evolution in dynamic scenarios. Particular emphasis will be devoted to data-driven formulations and on how to infer the unknown uncertainty models across sub-regions of interest while retaining formal statistical guarantees. Thereafter, the developed methods will be exploited to design efficient control algorithms for the safe deployment of autonomous systems in uncertain environments. The approach will combine techniques across applied mathematics and control engineering and include tools from dynamical systems, optimization, uncertainty quantification, optimal transport, and high-dimensional probability. The broader aim of the project is to derive inference methods and decision algorithms that rigorously address fundamental questions in uncertainty quantification and optimal control and apply them to domains like robotics and energy systems.

Deadline : 15 October 2023

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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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