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 Aerodynamics and Aeroelasticity of Extra-Large Wind Turbines
The energy transition necessitates a substantial expansion of wind energy, requiring a six-fold increase in the current installed wind energy capacity. Increased turbine sizes with larger rotor diameters that capture more wind power are an important cost reduction driver. It is foreseen that the rated capacity of OWTs will continue to grow, exceeding 25MW (over 350m rotor diameter) in the next five to ten years based on the current OWT design trends.
This PhD project is part of the NEXTgenT (NEXT generation of over 25MW offshore wind Turbine rotor design) MSCA Doctoral Networks Our goal is to train next generation of young engineers to understand technical and theoretical limits and challenge to design a next generation of over 25MW (25+MW) OWT rotor systems. This will be achieved by advancing the technologies, theories, numerical tools, and design methods.
The objective of this PhD position is to understand and modelling 3D unsteady aerodynamics and aeroelasticity of the extra-large wind turbine rotors. It will focus on:
- Understand the challenges of the aerodyanmic and aeroelastic design of the extra-large rotors;
- Developing novel unsteady 3D induction models;
- Dynamic testing of a scaled-down wind turbine in a wind tunnel;
- Validating the models at different scales under different operational conditions.
Being part of a MSCA Doctoral Network, you will have the opportunity to meet regularly and work closely with a group of your fellow PhD candidates, a distributed team of experts, both from academia and wind industry located all across Europe. You will also have the opportunity for two secondments (3 months each) in one industrial partner and one academic partner in the the NEXTgenT Doctoral Network..
Deadline : 31 October 2024
(02) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Application of Machine Learning to Wind Resource Assessment
This PhD is part of the EU MSCA Doctoral Network TWEED whose overarching objective is to train the next generation of excellent researchers, equipped with a full set of technical and complementary skills, to develop high-impact careers in wind energy digitalisation.
Increasing the accuracy and reducing the uncertainty of long-term wind resource estimates when planning wind farms has the potential to increase P90 values, reducing the cost of capital and thus investment costs. Only in the last few years, has attention been focussed on the application of machine learning (ML) to wind resource assessment. The traditional approach has focussed on the use of numerical models and relatively simple statistical techniques such as Measure-Correlate-Predict (MCP). The availability of large amounts of meteorological and digitised terrain data from terrestrial and remote sensing equipment (e.g., satellites, lidar) as well as the ever-increasing resolution of reanalysis datasets provide a rich source of information for site specific resource assessment. Indeed, the proliferation of wind farms can provide an additional source of data, though access to such data may be commercially restricted. The application of machine learning to unlock the potential of these data is at an early stage. The specific objective of this PhD position is to investigate how ML can be used to produce more accurate resource assessments as an alternative to physical modelling and as a supplement considering onshore and offshore applications.
Being part of a MSCA Doctoral Network, you will have the opportunity to meet regularly and work closely with a group of your fellow PhD candidates, a distributed team of experts, both from academia and wind industry located all across Europe. You will also have the opportunity for two secondments (3 months each) in one industrial partner and one academic partner in the TWEED Doctoral Network.
Deadline : 31 October 2024
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(03) PhD Degree – Fully Funded
PhD position summary/title: PhD position Classification, Modelling and Parameterisation of Tall Wind Profiles
The energy transition necessitates a substantial expansion of wind energy, requiring a six-fold increase in the current installed wind energy capacity. Increased turbine sizes with larger rotor diameters that capture more wind power are an important cost reduction driver. It is foreseen that the rated capacity of OWTs will continue to grow, exceeding 25MW (over 350m rotor diameter) in the next five to ten years based on the current OWT design trends.
This PhD project is part of the NEXTgenT (NEXT generation of over 25MW offshore wind Turbine rotor design) MSCA Doctoral Networks Our goal is to train next generation of young engineers to understand technical and theoretical limits and challenge to design a next generation of over 25MW (25+MW) OWT rotor systems. This will be achieved by advancing the technologies, theories, numerical tools, and design methods.
This PhD will investigate how a combination of profile clustering and numerical modelling can be used to provide a robust wind speed profile accurate to 500m using parameters that can be relatively easily inferred from observations or available reanalyses. These can then be used to assess turbine 25+MW turbine loading conditions for design and performance calculations. The main activities of this project will be: i) clustering of wind profiles into a small number of key classes, ii) numerical modelling to assess the dependency of tall profiles on different parameters, and iii) development of a robust tall profile model based on a small number of key, easy to measure variables.
Being part of a MSCA Doctoral Network, you will have the opportunity to meet regularly and work closely with a group of your fellow PhD candidates, a distributed team of experts, both from academia and wind industry located all across Europe. You will also have the opportunity for two secondments (3 months each) in one industrial partner and one academic partner in the the NEXTgenT Doctoral Network.
Deadline : 31 October 2024
(04) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Predictive Prognostics and Adaptive Load Control: An Integrated Approach to Wind Turbine Blade Longevity and Efficiency Optimisation.
IntelliWind is a Marie Sklodowska-Curie Doctoral Network funded by the Horizon Europe program. The project aims to train 16 highly motivated and exceptional PhD candidates. Its primary research objective is to reduce human involvement in decision-making and minimize the need for direct human interventions in operations and maintenance processes. This approach allows human resources to focus on more complex, better-planned, and efficient operations, leading to significant improvements in cost efficiency and reduced labor intensity in wind farm operations. The project will catalyze a shift in the skills and tasks required for wind power plant operations, moving from traditional engineering roles to the design, analysis, and interaction with automated machine algorithms.
Doctoral candidate position DC6 will be undertaken within the Intelligent Sustainable Prognostics (iSP) Group at the Faculty of Aerospace Engineering, Delft University of Technology, with collaborative placements at the Fraunhofer Institute for Wind Energy Systems IWES and the Technical University of Denmark.
Deadline : 31 October 2024
(05) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Artificial Physical Awareness Controller Synthesis
This PhD project is conducted within the ERC Consolidator Grant project “ARCS” in which you will collaborate with an international team of researchers to develop a new paradigm in control theory: Artificial Physical Awareness (APA). APA requires that the onboard autopilot has accurate and up-to-date knowledge of the safe envelope of the robot under its control. In this PhD project, a new APA-enabled controller will be developed that can effectively and efficiently exploit information contained in computed time-varying stochastic safe envelopes of autonomous robots after sustaining failures. The APA controller should be able to balance safety and performance based on mission requirements in nominal and off-nominal conditions. The new controller will be implemented on research drones, with which flight experiments will be conducted under nominal and failure scenarios to prove the APA principle.
Deadline : 29 October 2024
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(06) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Bio-inspired Safe Envelope Sensing and Upset Recovery
This PhD project is conducted within the ERC Consolidator Grant project “ARCS” in which you will collaborate with an international team of researchers to develop a new paradigm in control theory: Artificial Physical Awareness (APA). APA requires that the onboard autopilot can very quickly detect upsets and determine optimal recovery trajectories to prevent loss-of-control. In this PhD project, flying insects will be studied in collaboration with researchers from the University of Marseille with the goal of identifying and modelling their sensorimotor reflexes. In addition, experiments will be conducted with the aim of discovering whether flying insects employ adaptive internal models to compensate for injuries allowing for quick detection of, and recovery from loss-of-control. The principles behind these reflexes and adaptive internal models will be harnessed in a novel bio-inspired safe envelope detection and recovery methodology for autonomous robots, thereby providing a bio-inspired pathway to developing the APA autopilot.
Deadline : 29 October 2024
(07) PhD Degree – Fully Funded
PhD position summary/title: PhD position Machine learning for Human Behaviors in Negotiations and Deliberations
Negotiations and deliberations involve understanding and managing interpersonal dynamics, relationships, motivations, and navigating conflicts. While there has been much existing research in building negotiating agents, there remains a gap to integrate situated understanding of human behaviors and how these could affect negotiation and deliberation outcomes. What could be perceived from people’s interaction and communication patterns as well as decision-making across the negotiation and deliberation process? One solution is to leverage information gathered pre- and post- interaction, as well as multimodal data during the interaction process. As a member of this project, you will do research in the pipeline of multimodal machine perception and decision-making from data collection, annotation, algorithm and model design, to evaluation. The team you would join is a multi-disciplinary team, in which everyone is encouraged to take initiatives and progress in the work is achieved by shared ownership.
This project is part of a larger Groeifond consortium (Oncode Accelerator) which focuses on drug development and cancer research. This project is part of a work package that researches negotiations and deliberations between stakeholders in research and potentially clinical settings. This project aims to develop novel frameworks and learning approaches that integrate human behavior modeling to support decision-making in real-life scenarios.
The successful applicant will develop computational methods to model human behaviors using multimodal data – video and audio (e.g., speech and paralinguistics data), and to generate insights and feedback for descision-making, specifically for negotiation and deliberations in real-life situated settings.
The candidate will be embedded within the larger Interactive Intelligence Group at the Intelligent Systems Department at the TU Delft Electrical Engineering, Mathematics, and Computer Science faculty. For information about PhD at TU Delft, please visit https://www.tudelft.nl/onderwijs/opleidingen/phd
Deadline : Open until filled
(08) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Safe Learning for Interconnected Systems
The successful applicants will work on algorithms and techniques to make Safe Learning (SL) for Large Interconnected Systems a reality. This is a unique opportunity to develop and test new AI techniques for complex interconnected systems, such as those in future (6G and beyond) communications and in mobile & edge computing platforms.
Safe learning is one of the key research challenges in AI today. The lack of principled and practical SL algorithms prevents the use of AI in many critical application domains, especially those with strict quality-of-service criteria that operate under volatile and unforeseen conditions (e.g., the control of autonomous vehicles). The goal of these positions is to contribute to the development of the next generation of AI tools that are safe (i.e., respect system boundaries and operational requirements), robust against various sources of uncertainty, and prompt in adapting to environmental and mission changes. To this end, we envision using ideas and techniques from (but not limited to) the areas of optimistic learning, fair dynamic learning, robust learning, and continual learning, with applications to 6G and Edge AI resource control and decision-making problems.
The positions are part of a new Marie Curie Training Network called FINALITY, in which TU Delft joins forces with top universities and industries, including INRIA, IMDEA, KTH, the University of Avignon (Project Leader), the Cyprus Institute, Nokia, Telefonica, Ericsson, Orange, and others. The PhD students will have opportunities for internships with other academic and industry partners and will be able to participate in thematic summer schools and workshops organized by the project.
Deadline : October 29, 2024
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(09) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Scalable Graph Learning
Graph machine learning (Graph ML) is an emerging field of artificial intelligence (AI) motivated by the ubiquity of graph-structured data in real-world applications. Graph neural networks (GNNs) serve as a key technology in this area and are successfully used in recommendation systems, financial crime analysis, cybersecurity, and social and biological network analysis. However, conventional GNN architectures are limited in the type and complexity of graph patterns they can detect. For example, cycles, cliques, and frequent motifs can serve as highly discriminative signatures when analysing financial, biological, and social networks. However, cost-efficient and scalable discovery of such patterns in large graphs using neural-network-based approaches is challenging. Although these patterns can be detected using purely combinatorial approaches, such approaches lack statistical learning and adaptation capabilities and have high computational complexity.
A key objective of this project is to identify the trade-offs between combinatorial and neural-network-based approaches in the Graph ML space. We aim to combine and unify combinatorial algorithms and GNNs based on linear-algebra-based primitives, and achieve more efficient and scalable solutions.
Deadline : October 29, 2024
(10) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Stochastic Safe Envelope Prediction of Damaged Drones
This PhD project is conducted within the ERC Consolidator Grant project “ARCS” in which you will collaborate with an international team of researchers to develop a new paradigm in control theory: Artificial Physical Awareness (APA). APA aims to provide robots with accurate knowledge on their physical capabilities, which are reduced in currently unknown ways after faults and failures. APA requires accurate and up-to-date knowledge on the safe envelope, which is the region of the state space inside which safe operations can be guaranteed. In this PhD project, new theory and methods will be developed for computing, storing, adapting, and utilizing time-varying stochastic safe envelopes for autonomous robots under various failure conditions. In particular, experiments will be conducted with damaged drones in various flight labs, from which data stochastic safe sets will be computed and integrated with real-time adaptive flight envelope protection controllers.
Deadline : 29 October 2024
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(11) PhD Degree – Fully Funded
PhD position summary/title: PhD Position on Socio-Economic Tipping Points and Positive Levers in Scaling up Nature Based Solutions
Climate change, loss of biodiversity and resulting economic losses, pose an increasing threat to the prosperity and well-being of current and future generations. To be able to live and work safely and healthily in the Netherlands in the future, fundamental changes are needed in our land use and soil and water management. It is internationally recognized that Nature Based Solutions (NBS) – integral solutions that harness the power of nature to e.g. reduce climate and environmental problems in a sustainable manner – can play an important role in this transition.
NBS are at the core of climate policies, especially for climate change adaptation (e.g. the EU Adaptation Strategy). While their ecological and societal benefits are well known, NBS often compete for space and face land use trade-offs with various economic activities. Furthermore, at times short- and mid-term benefits for either local public (e.g. regional or municipal governments) or private actors (e.g. households and companies) are unclear or not yet realized. These two problems jointly create barriers to scaling up and speedy uptake of NBS. At the same time, NBS and nature, in general, are argued to become new critical ‘asset class’, increasingly attracting the attention of financial investors and calling for quantitative assessments of mechanisms to generate, maintain and distribute economic value of NBS. Even the high-level COP meetings call for identifying positive tipping points for NBS uptake and scaling up. Such analysis needs to rely on the existing, largely qualitative NBS frameworks, and go beyond to develop quantitative methods to assess benefits for various private and public actors across timescales (short-, mid- and long-term) and the distribution of both benefits and costs among actors to give guidance for the design of cost-effective NBS investment policies.
This PhD project will focus on the socio-economic and financial tipping points and positive levers in scaling up NBS. This requires mapping short-, mid- and long-term benefits and costs for private and public actors. A major part of this PhD research project will focus on computational agent-based modeling to capture the mechanisms that potentially drive tipping to NBS adoption that are socially acceptable, as well as economically and financially feasible for private and public actors. The PhD student will develop and apply an agent-based model to identify the distributional effects of NBS for various actors, and to explore under what circumstances positive socio-economic tipping points for NBS adoption emerge for a selection of NBS across policy scenarios.
Deadline : 28 October 2024
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(12) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Public Engagement and Geothermal Energy
As part of a large European transdisciplinary research project, we are looking for a candidate to investigate how local cultures affect perceptions of geothermal energy.
Based on a literature review, you will design a questionnaire that you will distribute across different countries. You will analyse the data and report on the findings in scientific publications.
Furthermore, you will develop a manual for stakeholders (such as energy companies) who engage with local communities. You will experimentally test the effectiveness and quality of the manual with randomised controlled trials.
Deadline : 27 September 2024
(13) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Quantifying the National and Regional Distribution Effects Of Private and Public Adaptation Action
We are looking for a passionate and ambitious researcher to join our team and contribute to understanding how climate finance shapes adaptation and how these adaptation measures, in turn, influence social and economic resilience to climate change. This PhD position will focus on climate physical risks, analyzing the effectiveness of various climate finance mechanisms in supporting adaptation, and exploring socio-economic implications of climate adaptation measures at different scales. This project will target regional economic assessments and financial instruments for climate risks for the Dutch context. This position offers a unique opportunity to perform impactful work at the intersection of advanced academic research and policy-oriented applications. Your research will help shape climate finance policies and instruments, contributing to achieving resilience to climate change across different countries, regions, and sectors.
The research will target the development and evaluation of innovative financial instruments and adaptation strategies aimed at mitigating climate risks. This includes leveraging the latest socio-economic and climate scenarios to understand regional vulnerabilities and resilience. Additionally, you will investigate the role of private adaptation actions and climate finance in enhancing economic stability and growth. This position offers a unique opportunity to perform impactful work at the intersection of advanced academic research and policy-oriented applications, with the potential to shape climate finance policies and instruments across different countries, regions, and sectors.
Deadline : 27 September 2024
(14) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Effective Metadata Management for Data-Driven Plant Resilience Enhancement
We are seeking a highly motivated PhD candidate to join our research team focused on Effective Collaborative Metadata Management for Large Data Repositories. The project is part of the Crop-XR program, a highly collaborative 10-year national initiative of universities and industry, with a mission to grow resilient crops developed by data-driven design and AI plant models.
This exciting opportunity will involve analyzing the research gaps for establishing effective metadata management in large data repositories. The most promising identified gaps will be addressed by developing novel innovative workflows and technical solutions.
The goal is to improve the practices of metadata management in plant sciences along the FAIR principles (Findable, Accessible, Interoperable, Reusable) such that complex (AI) models for improving plant resilience can be reliably developed, trained, improved, and deployed.
The models are developed by our partners to predict how plants respond to environmental stresses such as drought, malnutrition, and extreme temperatures. This will allow plants to withstand combinations of stresses brought by a changing climate.
Deadline : September 27, 2024
(15) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Improving the Quantification and Mitigation of the Climate Impact of Aviation Contrails by Use of Observational Data
Contrails are the line-shaped clouds that trail behind aircraft because of mixing of the warm, moist engine exhaust with the colder, drier ambient air. Under the right conditions, these clouds can persist for multiple hours and spread into large decks of contrail cirrus, which are currently estimated to have a climate impact comparable to that of aviation-emitted CO2. Several mitigation pathways are being explored, such as the use of alternative fuels and changes in aircraft routing. Especially the latter option, which would involve routing aircraft around ice supersaturated regions, could be a short-term solution to eliminate a large portion of aviation’s climate impact. However, these approaches are currently limited by the accuracy of weather predictions and contrail models. The objective of the thesis is to utilize observational data of contrails, such as satellite images, to further quantify the capabilities of these models and improve them where possible.
Deadline : 26 September 2024
(16) PhD Degree – Fully Funded
PhD position summary/title: PhD Position New Luminescent Materials: Towards Electricity Generating Windows
You will combine your creativity with analytical- and experimental skills to discover new electricity generating luminescent window coatings. You will publish new fundamental solid state physics and optics processes and explore options for scale-up of your new coatings with a start-up and the glass industry. More info on the topics of the research group can be found at www.vanderkolklab.com
In this PhD project you will develop luminescent materials for building integrated photovoltaic (BIPV) technology. You will make and study new types of strongly absorbing luminescent solar absorber materials of just a few hundred nm thick, that can convert the UV+VIS part of the solar spectrum into infra-red luminescence. When applied as a coating to windows, these materials can enable a cost-effective electricity generating PV-window following the principle of a Luminescent Solar Concentrator (LSC). A LSC can harvest sunlight by absorbing, re-emitting, and subsequently guiding light, like in an optical fibre, to solar cells integrated in the windowpane that convert the light in electric power.
To successfully develop new valuable luminescent absorber materials, it is crucial you will get fundamental understanding of the physical processes underlying the luminescence mechanism of the materials. One of the scientific challenges is to understand how generated electron-hole pairs can transfer their energy to the luminescence centres. The strongest possible absorptions in an inorganic material are so-called bandgap absorptions in which an electron is excited from the valence band (VB) to the conduction band (CB) leaving behind a hole in the VB. Although there are many materials (hosts) with a small bandgap that absorb the entire visible part of the solar spectrum (black materials), very few show efficient luminescence of doping ions. Host-to-doping ion energy transfer is often described by a resonant process between (self-trapped) exciton emission and doping ion absorption. The materials of this project have a smaller bandgap and are selected to have a small exciton binding energy causing transfer involving excitons to be inefficient. In the new materials, sequential transfer of first the electron followed by the hole is the anticipated transfer process to the luminescence centres.
The fundamental insights are obtained first by time and temperature resolved optical and luminescence spectroscopy combined with a variety of techniques to analyse the structure, (defect) composition and morphology of the films. Secondly fundamental understanding involves data interpretation and model development using knowledge of solid-state physics, optics and quantum mechanics. Ideally the obtained insights will be used to select other materials with improved properties during your project.
In this project you will learn the ins and outs of reactive DC, RF and pulsed magnetron sputtering, the working horse technology in glass coating industry, to make the luminescence materials. Targeted materials are rare earth (like Yb) and transition metal (like Mn) doped inorganic semiconducting materials, emitting in the infra-red spectral range, where silicon solar cells have high conversion efficiency.
The Luminescence Materials group at Delft University of Technology has more than 30 years of experience with luminescent materials research and collaborates with a start-up company and glass coating industry to facilitate a route to large scale application of the coatings as windows. You will be working in a team, headed by your promotor, with other PhD’s, technicians, a start-up company and glass coating industry. A PhD student receives a general education by the University Graduate School and can be involved in activities like teaching practicals for a small part of your time and co-supervise graduation project at the bachelor and master level.
Deadline : 26 September 2024
(17) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Physics-Aware Foundation Models for Climate Research
The emergence of general-purpose large AI models, known as “foundation models” (FMs), such as GPT-n and Llama for language, and DALL-E for images, marks a significant advancement in AI. However, their application to critical areas like climate, environment, and natural energy often lacks interpretability and reliability, largely due to their data-driven nature. Integrating principles of physical consistency into FMs is crucial to ensure that the models not only perform well on data but also adhere to the fundamental principles of the systems they aim to model.
This PhD project will address the following fundamental challenges on how to achieve this for practical large-scale systems.:
- Developing a large-scale physically consistent FM: You will integrate atmosphere, surface, and subsurface earth observations to develop FMs for modelling the erath and climate system that incorporate multiple physical principles, especially considering system dynamics. You will explore incorportating prior physics knowledge throughg the use of datasets, loss functions, or model architectures.
- Balancing interpretability and generalization: While adding physical constraints can improve interpretability and reliability, it can also potentially restrict the model’s ability to generalize to diverse datasets or adapt to novel scenarios. You will investigate balancing between incorporating constraints and maintaining flexibility from a more fundamental aspect.
- Enhancing Sustainability: You will investigate how to reduce the energy consumption associated with training and maintaining FMs, addressing urgent climate challenges.
Deadline : September 27, 2024
(18) PhD Degree – Fully Funded
PhD position summary/title: PhD position Trustworthiness of Auto-Generated Systems
We are seeking a motivated PhD student to work on the challenge of ensuring trustworthiness for auto-generated software.
The increasing reliance on third-party software, including the use of AI agents for code, significantly enhances the efficiency of software development. However, this also introduces substantial trust issues concerning the safety, security, and correctness of the final product.
The goal of this PhD project is to design language-agnostic abstractions and technologies that enable the safe and secure integration of third-party code into existing projects.
This project bridges the software engineering requirements with the formal methods guarantees. The prospective PhD student will be using their deep understanding of programming languages, formal specifications, and program analysis to, first, formalize the challenges arising from integrating code into evolving software, and, second, to design and implement solutions to address these challenges.
Deadline :September 26, 2024
(19) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Value-Based Assessment Methods for AI Systems
AI alignment refers to the goal to make AI systems behave in line with human intentions and values. With the rapid proliferation of AI systems and their growing capability, there are equal risks from misalignment. Validating models is a crucial step before decisions can be made about implementation and is important for continuous monitoring of systems in use. The challenge is, however, that validation needs to happen along a range of different values that are important for AI to possess at the police: accuracy, but also fairness, reliability, trustworthiness, and more need to be ensured.
As a PhD student at TU Delft, you will conduct impactful research on two key aspects in advancing the responsible use of AI within the police force. First, you will investigate the standards and values surrounding AI usage, particularly concerning openly available models to begin with. This entails defining what criteria these models must meet, beyond common considerations like bias and fairness. You will research which values AI systems need to be aligned with through in-depth case studies at the Police. Second, you will also design methods to systematically evaluate a range of models against these established standards and values. This contribution ensures the responsible deployment of AI within the police force, pushes forward our understanding of how to align AI models in practice, and maximizes the efficiency of utilizing publicly available models.
Through these efforts, you will significantly contribute to advancing the important area of AI alignment and help foster an ethical and accountable AI culture within the organization.
Your project is part of the Model-Driven Decisions Lab, a Police-TU Delft initiative, where you will join an interdisciplinary community of four fellow PhD students. Together, you will share knowledge to tackle AI-assisted decision-making from different perspectives. To foster close collaboration with the stakeholders and work on practical implementation, you will spend 20% of your time at the National Police’s strategy and innovation division. Given the ethical and moral facets of your research, you will also work closely with colleagues of the Delft Digital Ethics Centre at the Faculty of Technology, Policy, and Management (TPM). Your home base will be the Web Information Systems research group at the Computer Science faculty (EEMCS). As an internationally diverse team of driven academics and students, we cultivate a welcoming and collaborative environment. We will give you all the support and training you need to evolve both personally and professionally.
Deadline : September 26, 2024
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(20) PhD Degree – Fully Funded
PhD position summary/title: Two (2) PhD Positions on Algorithms for Formal AI Verification and Explainability
You will conduct both theoretical and empirical research at the intersection of logic, optimization, machine learning, control, monitoring, interpretability, and visualization. Both PhD projects are inspired by real-world deployment of AI, with one leaning towards advancing theory and the other to be done in close collaboration with domain experts from the Netherlands Railways (NS), leading to significant scientific as well as practical impact.
You will be part of the Algorithmics Group in the Department of Software Technology of the Faculty of Electrical Engineering, Mathematics and Computer Science. You will work in a dynamic and diverse environment of other PhD and postdoc researchers excited about making theoretical and algorithmic contributions in intelligent decision making.
Deadline : September 26, 2024
(21) PhD Degree – Fully Funded
PhD position summary/title: PhD Position – System integration with demand side and industrial transformation – OrangeWind project
We are looking for a researcher who:
– wants to contribute to the energy transition
– has a background and proven experience in process modelling (e.g ASPEN) and other system modelling techniques as well as programming techniques (e.g. Python)
– has an academic background at master level in e.g, chemical engineering, process systems engineering, or environmental sciences with a natural science basis.
– has strong analytical and communication skills.
– enjoys working in an interdisciplinary group and is a team player.
Very good communication and writing skills in the English language are required.
Deadline : Open until filled
(22) PhD Degree – Fully Funded
PhD position summary/title: PhD Position 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 :25 September 2024
(23) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Robust ML-Driven mmWave Communication and Sensing in Smart Environments
Are you interested in pushing the boundaries of 6G wireless communication and sensing in one of the top engineering schools in Europe? As a part of an international team, you’ll investigate new solutions for 6G systems. You’ll have the chance to work across disciplines on the latest challenges of wireless communication and sensing systems. Whether you have a theoretical background (optimization, machine learning, signal processing) or an experimental background (software-defined radios, RF engineering), you build upon your strengths within the the 6thSense MSCA Doctoral School. Working across disciplines, our team has joint projects with top international research institutions and industry leaders in telecommunications, including Nokia, Bosch, and NI. By joining our team, you benefit from a highly competitive salary, a state-of-the-art lab equipped with high-end SDRs, and a customized soft-skill training program, preparing you for your career beyond your Ph.D.
In addition the position includes two unique research visits:
- Visit to Prof. Yasaman Ghasempour’s lab at Princeton University for 5 months
- Visit to Nokia Bell Labs, Stuttgart for 3 months.
This PhD position focuses on “robust mmWave communication and sensing in smart environments”. The PhD position involves developing algorithms to detect and track objects using Doppler shift and Angle of Arrival (AoA) analysis of Channel State Information (CSI) at mmWave receivers, dynamically configuring and allocating Reconfigurable Intelligent Surface (RIS) elements for optimizing sensing and communication tasks, and evaluating solution performance on TU Delft testbed facilities.
Your home base will be the Embedded Systems group of TU Delft, where you will be supervised by Dr. Arash Asadi.
Deadline : September 24, 2024
(24) PhD Degree – Fully Funded
PhD position summary/title: 2 PhD Positions Fluid Dynamics and Soft Matter of Complex Multicomponent Emulsions
Two PhD positions are available in the Garbin lab (https://garbinlab.org) on the NWO-Vici project “Flow physics of Pickering emulsion reactors for sustainable chemical conversion”. As part of an interdisciplinary team, you will explore the interplay of chemical reaction and transport phenomena in nanoparticle-stabilized emulsions for chemical conversion. We are currently looking for 2 PhD candidates to join the project team, which already comprises 3 postdocs with complementary expertise and working in close collaboration. As a PhD candidate working on this project, you will unravel the flow physics of multicomponent emulsions stabilized by nanoparticles (known as “Pickering emulsions”) and their dynamic evolution during chemical reactions. Although these complex emulsions have proven potential for sustainable chemical conversion, the effects of mass transfer and convection in combination with chemical reactions remain poorly characterized and understood for these systems, which currently limits their potential for applications.
Deadline :23 September 2024
(25) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Synthetic Cell Research in the Gijsje Koenderink Lab
We seek an outstanding experimental biophysicist with a strong affinity for research at the interface of physics and biology and with relevant research experience in fields such as biophysics (membrane, molecular, or cell biophysics), synthetic biology, or soft matter science. We are looking for a candidate with a high level of intellectual creativity, genuine interest in fundamental research, who enjoys being part of a collaborative international team and easily communicates with scientists from different disciplines.
Deadline :23 September 2024
(26) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Assessing the Effects of Technology on Youth Loneliness
The departments of Cognitive Robotics and Biomechanical Engineering at the Delft University of Technology invite applicants for a PhD position in the project, “Disconnected: Assessing the Effects of Technology on Youth Loneliness”.
This project has been granted funding from NWO under the Dutch Research Agenda (Nationale Wetenschapsagenda; NWA), a program that focuses on key questions that have been established with input from citizens. Our project is aimed at scientific inquiry regarding loneliness in the target group of 18–26 years olds, and is part of a wider network of projects that address loneliness in various target groups.
Our consortium, consisting of Dr. Dimitra Dodou, Dr.ir. Yke Bauke Eisma, and Prof.dr.ir. Joost de Winter (TU Delft), and Prof.dr.ir. Nederveen (AMC), supplemented by various partner organizations in the field of technology and society, hypothesize that ‘technology’ plays a central role in the potential causes of loneliness, because technology can possibly create a distant form of inter-human connection. At the same time, technology may also provide a solution by connecting with people in a meaningful way. Through this project, we aim to identify the deeper causes of loneliness in society, and in this way, come up with valid recommendations, and solid scientific and empirically based knowledge about loneliness. Furthermore, we actively strive to reduce stigmas about loneliness in a well-informed manner.
As a PhD candidate, you will be responsible for executing the ‘Technology and Loneliness’ work package. During your PhD, you will examine the current use of technology among young adults, and the direct (short-term) effects of existing technologies such as smartphones and new technologies like chatbots, embodied robots, and augmented reality. You will conduct research to investigate the direct effects on subjective experiences and explore and propose new technological solutions to mitigate loneliness. You will be closely working with two other PhD candidates in the project, one at the TU Delft and another at the AMC.
Deadline : 21 September 2024
(27) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Embedded AI for 6G Networks
Join the frontier of innovation in 6G: the future of mobile networks technology! In the Netherlands, a unique alliance of 60 top-notch ICT companies, semiconductor firms, and research institutions has united to spearhead specific aspects of 6G: (1) software antennas, (2) AI-driven network software, and (3) groundbreaking 6G applications. Join us as a PhD student in this prestigious Future Network Services (FNS) flagship project, where research and entrepreneurial pursuits converge.
6G networks will utilise frequencies from sub 6 GHz to mmWave and THz bands, thereby substantially increasing the number as well as the heterogeneity of the required base stations. This complexity is further exacerbated by (1) the emergence of new network architectures like cell-free networks, (2) novel network services, such as joint communication and sensing, and (3) the evolution to space-air-ground networks. Amidst this complexity, edge AI emerges as a promising solution directly integrated into hardware devices at the edge. Your PhD research revolves around optimising these AI algorithms for base stations with different hardware and computing capabilities. Work on advanced edge/embedded AI models tailored for managing the densely packed frequency bands in 6G networks, and enabling seamless connectivity across space, air, and ground base stations. Your work takes a novel approach by co-designing these models alongside the hosting hardware, ensuring optimal performance and scalability. The result? Hardware-aware, adaptable AI models attuned to the dynamic nature of 6G networks, adjusting model complexity based on available computing resources.
Your home base will be the Embedded Systems group, where you will be supervised by Dr. Qing Wang. You will also receive guidance from Prof. Fernando Kuipers and the Networked Systems group. Hence, you will have ample support and opportunities for riveting sparring and knowledge sharing.
Deadline : Open until filled
(28) PhD Degree – Fully Funded
PhD position summary/title: NExTWORKx PhD position Safe Use of AI in Telecommunication Networks
NExTWORKx*is the strategic partnership between the Telecom and ICT service provider KPN and Delft University of Technology. NExTWORKx aims to develop new concepts & technology in telecommunication and artificial intelligence, to respond to new disruptive technologies and to continue the development of talent. NExTWORKx enters its second research phase with a focus on developing and applying concepts from network science to model the huge number of real-time interactions between software components and data sets and to enable monitoring and control. AI-based algorithms will be developed for monitoring information flows and smart contracts. A trusted layer for monitoring and control is crucial for every network and digital infrastructure individually, but also across critical infrastructures, data sharing and smart city platforms. NExTWORKx announces the hiring of 4 PhD students who will be mentored by both TUDelft and KPN staff. The 4 PhD projects contribute to the above goals, but each with a different focus:
- Safe use of AI in telecommunication networks
- Resilient cross domain networks for critical infrastructure
- Secure data sharing in federated edge clouds
- Co-evolution of telecommunication networks and urban systems
Deadline : September 15, 2024
(29) PhD Degree – Fully Funded
PhD position summary/title: PhD position: Pathways for Realising Climate Adaptation in the Wadden Sea (PaRCA)
We are seeking a PhD candidate for PaRCA, a project within a German-Dutch coperation programme “Pathways for Realising Climate Adaptation in the Wadden Sea”. In our project we seek to develop and evaluate measures for mitigating the effects of climate change on the Wadden Sea. We focus on the the morphodynamics and long-term morphological development under influence of sea level rise and human interference. Multiple PhDs and Postdocs at various universities and research institutes in the Netherlands and in Germany will work on this challenge and develop new insights in the societal challenges regarding sediment management, ecology and flood safety in the Wadden Sea. The topic of this PhD project focusses on the respons of the Wadden Sea to accelerating sea level rise and human interventions.
Deadline : 15 September 2024
(30) PhD Degree – Fully Funded
PhD position summary/title: PhD Position: Insight into Urban Water Vapour
Climate change is already affecting the way we as a society experience the weather. More extremes in temperature, rainfall and wind means we need to adapt ourselves and our cities to the changing climate. While the Urban Heat Island is a well-known driver of heat stress, urban climate processes related to water are less explored, especially the water vapour content of the air. Water vapour is a driving component of heat stress, since it influences the human ability to cool down through sweating. Given that climate adaptation measures in cities often go towards blue-green solutions (water and vegetation), it is important to quantify how these adaptation measures will affect the local urban atmosphere, and impact people’s lives. There are many unanswered questions regarding the importance and the time evolution of urban water vapour, and how to make use of urban blue and green spaces to combat (future) heat stress.
Deadline : 15 September 2024
(31) PhD Degree – Fully Funded
PhD position summary/title: PhD Position The Impact of Circular Measures on the Life Span of Buildings
In order to make our cities more circular, we need to fundamentally revisit the way that we define and quantify the value of our built environment. This fully-funded doctoral project is part of a Marie Sklodowska Curie Action Doctoral Network, QuiVal, a programme funded by the European Union with the aim to develop a fundamentally new approach to real estate valuation, allowing the sector to rapidly transition towards a more sustainable, low carbon and circular future. This will be achieved through a transdisciplinary doctoral training programme across 8 universities and 14 industry partners, and including 13 doctoral candidates who will work, together and separately, to rethink the philosophy, principles and processes of valuation. The programme is transdisciplinary and co-produced by leading academics and practitioners who are passionate and knowledgeable about the real estate industry and about sustainability. Five objectives have been developed through which to achieve the aim, and each doctoral project will respond to one of these objectives. For an overview of all the vacancies of QuiVal, click here.
The doctoral project at Delft University of Technology (research project 2a) is part of the response to the objective, ‘Value of the building that lasts’. This project will study how exchangeability of building functions and uses contribute to increase buildings’ functional lifespan (i.e. reduce functional obsolescence), and how a prolonged lifespan could increase the value of the building. Here we will develop knowledge about adaptive reuse, circularity and building adaptability to expand the lifespan of real estate and contribute to climate adaptation and mitigation. This project innovatively uses the quantum perspective to answer (1) how to increase the adaptability and lifespan of existing real estate through adaptation and adaptive reuse strategies, (2) which strategies should be applied to achieve future ready real estate, and (3) how will an extended building lifespan contribute to the future value of real estate. This project will follow a research by design methodology, developing and testing new solutions through co-creation, involving all partners of the research consortium. The project explores, develops and tests the value of different uses of a building. Furthermore, this project will develop knowledge about the costs of measures of particularly circularity and building adaptability to finally develop an assessment framework for investing in lifespan extension underpinned with real options appraisal. As well as a dissertation, and a number of academic papers, the project will produce an accessible report for real estate professionals.
You, the doctoral candidate (DC), will spend the majority of the funded three years at Delft University of Technology in Delft, the Netherlands, but will also be funded for two periods on secondment to QuiVal partners, one to industry partner Reborn and the other to university partner University of Southern Denmark in Odense, Denmark. There will also be significant opportunities for sharing knowledge and fostering discussions with the other 12 DCs, through five workshops and three summer schools. The DCs will also work together to collect data and collaborate with stakeholders including a network of industry frontrunners who are partners in the programme, increasing understanding, research impact, and chances of employment in the real estate and related sectors.
Deadline : 15 September 2024
(32) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Surrogate-enabled Uncertainty Quantification for Water Quality Modeling
Reactive transport models are essential for assessing groundwater quality and contamination risks, which are critical for safe drinking water. However, uncertainties in model parameters and data hinder accurate predictions. Traditional uncertainty quantification (so-called Bayesian) methods require extensive computational resources, hindering robust uncertainty quantification for complex models.
This PhD position offers an exciting opportunity to address this challenge by leveraging surrogate modeling techniques. Surrogate models approximate computationally intensive models at reduced cost, enabling uncertainty quantification despite time budget constraints. Recent advances in machine learning techniques also offer promising opportunities to improve surrogate modeling approaches. In this project, you will develop surrogate models tailored to reactive transport models and integrate them into an uncertainty quantification framework.
Deadline : 15th of September 2024
(33) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Physics-Enhanced Machine Learning for Resilient Railway Infrastructures
Resilient railway infrastructure is vital for ensuring safe, reliable, and sustainable transportation services. However, maintaining infrastructure resilience amidst emerging challenges is essential. These challenges include mitigating the impact of extreme weather events and natural disasters, addressing ageing infrastructure, optimizing maintenance schedules to minimize disruptions, enhancing safety measures to prevent accidents, and adapting to changing operational demands and technological advancements. Overcoming these challenges requires interdisciplinary approaches that integrate cutting-edge technologies, such as physics-enhanced machine learning, with traditional engineering expertise to endure, predict, prevent, and respond to potential threats to railway resilience effectively.
This project aims to improve the resilience of railway infrastructures by leveraging physics-enhanced machine learning. You will integrate state-of-the-art physics knowledge that governs infrastructure mechanics and dynamics with the big data from infrastructure monitoring systems. You will develop innovative physics-enhanced machine learning algorithms and models to address risks and uncertainties associated with the operational and environmental conditions of railway infrastructures. As part of this project, you will engage with railway infrastructure managers to validate your proposed solutions for resilience improvement.
As a PhD student, you will be a member of the Railway Engineering Section in close cooperation with the Delft Center for Systems and Control and the Dutch Railway Infrastructure Manager, ProRail. Railway is the backbone of sustainable mobility. The section of Railway Engineering deals with the physical assets of the railway system, including trains, power supply systems, track, as well as the interfaces and dynamic interactions between them. The research, innovation, development and education of the section concern the whole life cycle of the assets, from design, construction, operation, degradation, monitoring, maintenance to retrofit, as well as the data-driven intelligent management of the assets, taking into consideration of the performance of the whole railway system. The section plays a leading role in the TU Delft Rail Institute (DelftRail).
The Department of Engineering Structures focuses on the development of resilient, smart and sustainable structures and infrastructures. Our aim is to meet societal demands in transportation, the energy transition and sustainable reuse. Research themes include dynamics of structures, mechanics of materials related to e.g. climate change, modelling and design of railway systems, multi-scale modelling of pavement materials and structures, reuse of materials, structures and parts of structures, assessment methods for structures, smart monitoring techniques, design methods, replacement and renovation of civil infrastructure and development of new materials and maintenance techniques. Our unique Macro Mechanics Laboratory facilities support full-scale testing, monitoring and modelling of structures to facilitate implementation of innovations. The department delivers both groundbreaking research and world-class education for undergraduate and graduate students. As a team, we represent different backgrounds, skills and views. We foster an inclusive culture, as our combined identities, attitudes and ambitions widen our perspective and make up our strengths..
Deadline : 15 September 2024
(34) PhD Degree – Fully Funded
PhD position summary/title: PhD Position New Approaches to Options Valuation for Circular Real Estate
In order to make our cities more circular, we need to fundamentally revisit the way that we define and quantify the value of our built environment. This fully-funded doctoral project is part of a Marie Sklodowska Curie Action Doctoral Network, QuiVal, a programme funded by the European Union with the aim to develop a fundamentally new approach to real estate valuation, allowing the sector to rapidly transition towards a more sustainable, low carbon and circular future. This will be achieved through a transdisciplinary doctoral training programme across 8 universities and 14 industry partners, and including 13 doctoral candidates who will work, together and separately, to rethink the philosophy, principles and processes of valuation. The programme is transdisciplinary and co-produced by leading academics and practitioners who are passionate and knowledgeable about the real estate industry and about sustainability. Five objectives have been developed through which to achieve the aim, and each doctoral project will respond to one of these objectives. For an overview of all the vacancies of QuiVal, click here.
The doctoral project at Delft university of Technology (research project 1a) is part of the response to the objective, ‘Quantum inspiration and implementation’. This project will explore improvements to valuation and decision-making of real estate on a multitude of parameters. The aim is to make real estate investment decisions more adaptive, by using the time dimension and real options analysis in valuation methods. Time value of money and real options analyses are strongly affected by linear assumptions in investment calculations, for example through interest rates that are smoothened over time. Quantum mechanics can contribute to break open this linearity, as it conceptualises a non-linear world to pioneer models for capturing value assumptions in dynamic and fuzzy real-world conditions. The project will conceptualise new innovative models and analyse how these can more accurately reflect real estate valuation in a circular economy for different industry players. As well as a dissertation, and a number of academic papers, the project will produce an accessible report for real estate professionals.
You, the doctoral candidate (DC), will spend the majority of the funded three years at Delft University of Technology in Delft, The Netherlands, but will also be funded for two periods on secondment to QuiVal partners, one to industry partner Rabobank and the other to university partner Tallinn University of Technology in Tallinn, Estonia. There will also be significant opportunities for sharing knowledge and fostering discussions with the other 12 DCs, through five workshops and three summer schools. The DCs will also work together to collect data and collaborate with stakeholders including a network of industry frontrunners who are partners in the programme, increasing understanding, research impact, and chances of employment in the real estate and related sectors.
Deadline : 15 September 2024
(35) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Jointly Optimizing Waveforms, Beamforming and Radio Resource Management for Joint Communications and Sensing in 6G Networks
TU Delft’s Microwave Sensing, Signals and Systems (MS3) group and TNO’s (Dutch Organization for Applied Scientific Research) Networks department combine forces in the prestigious Future Network Services (FNS) flagship project to develop innovative solutions enabling joint communications and sensing (JCAS) in 6G networks.
In JCAS, communication and sensing services share a common network infrastructure and radio resources, with the technology and associated management solutions designed to flexibly achieve tailored trade-offs between energy consumption, resource utilization, communications (e.g. user throughput, latency, reliability) and sensing performance (e.g. resolution, probability of detection). We offer a Ph.D. research project focusing on joint optimization and assessment of waveform design, beamforming strategy and radio resource management mechanisms (e.g. power control, antenna aperture sharing, beamforming, scheduling, adaptive waveform, handover control) on communication and sensing performance, aiming to optimize the utilization of the shared radio resources for dual-functional joint communication and sensing systems with balanced performance trade-offs!
Deadline : September 15, 2024
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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