Eindhoven University of Technology, 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 Eindhoven University of Technology, Netherlands.
Eligible candidate may Apply as soon as possible.
(01) PhD Degree – Fully Funded
PhD position summary/title: 11 PhD positions in the excellence & sustainability programme of the section Mechanics of Materials of TU/e
The PhD projects listed below are embedded in 4 larger programmes:
- Green Steels: The Dutch steel sector faces a major transition. The production, processing, use and recovery of steel is to be made significantly more sustainable by 2030 and completely CO2 neutral by 2050. The programme “Growing with Green Steel” is a plan to achieve this, involving major changes throughout the steel value chain. The section Mechanics of Materials contributes to this plan by studying how the microstructure and resulting properties of green steels are being affected by the new steel processing routes.
- Physics-Based Design of Hydrogen-Resistant Steels: The shift to a hydrogen-based energy system brings a major materials challenge: hydrogen can penetrate steel and make it brittle, leading to sudden failure. This is especially challenging for sustainable (‘green’) steel grades, which exhibit a complex microstructural variability. This programme addresses this challenge using tools at the intersection of materials physics, computational modelling, digitalisation and targeted experiments. Using physics-based models linking microstructural mechanisms to macroscopic behaviour, and informed by experimental characterisation and validation, digital twin frameworks are developed enabling a virtual assessment and optimisation of steel microstructures before they are produced. This programme is therefore essential for the future hydrogen economy.
- Thermal interfaces at cryogenic conditions: Many advanced technologies — like quantum computers, powerful microscopes, and chip-making tools — require extreme cooling. However, the optimal design of cooling systems at cryogenic conditions is hampered by the lack of predictive thermal conductance models at these temperatures. This results in costly trial-and-error development, slows innovation, and ultimately in system designs with suboptimal thermal performance and energy inefficiencies. This programme focuses on the development of multiscale models that will improve our understanding of how microstructural changes in materials and evolving constrained contact conditions at cryogenic temperatures affect thermal and mechanical properties and uses that knowledge to build smarter, quieter, and more energy-efficient cooling systems. These new systems will support better medical imaging, faster computers, and greener high-tech manufacturing.
- Wafer handling: Silicon wafers are the base material for the fabrication of modern electronic devices. To ensure optimal reliability of the adopted lithographic processes, two aspects are important: (i) the surface quality of the silicon wafers needs to meet stringent requirements and (ii) the production environment needs to be absolutely immaculate. Both of these aspects constitute the driving force for extensive investigations of silicon under contact loading conditions in this programme. Typically, the influence of mechanical interaction on silicon wafers is investigated by means of advanced scratch experiments under high-resolution observation, that are essential for gaining an improved mechanistic understanding at the microscopic scale.
All PhD projects involve collaborations with industry. These are indicated between square brackets below, along with the name of main supervisor of each project.
Deadline : 30-09-2026
(02) PhD Degree – Fully Funded
PhD position summary/title: PhD Diagnostics for interconnected complex dynamical systems
We invite highly motivated students with a strong background in mathematical control theory, and a keen interest in machine learning to apply for the PhD position within the Dynamics and Control section at the Department of Mechanical Engineering, Eindhoven University of Technology. The mission of the Dynamics and Control Section is to perform research and train next-generation students on the topic of understanding and predicting the dynamics of complex engineering systems in order to develop advanced control, estimation, planning, learning and diagnostics strategies which are at the core of the intelligent autonomous systems of the future: Designing and realizing smart autonomous systems for industry and society.
Complex dynamical systems, such as semiconductor equipment, consists of many interconnected modules, which are functionally, digitally and physically interconnected. The throughput of the equipment relies on continuous runtime while meeting stringent requirements on accuracy and performance. Therefore, monitoring the health of these systems is crucial, which is now largely performed by human experts. The aim of this PhD project is to design innovative monitoring algorithms for complex dynamical systems to automate fault isolation with diagnostics performance guarantees.
Current health monitoring technology is typically designed for either (i) individual system components/modules (which would require removing the component from the system, which is not possible in practice), or (ii) for the system as a whole. The first approach fails to account for the interaction between modules and its effect on the whole system. The second approach makes it challenging to isolate which module may be failing and how to zoom in on the part of the system that is the root cause of the failure. This PhD position will address this challenge by developing hierachical diagnostic tools for complex dynamical systems.
Deadline : 17-05-2026
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(03) PhD Degree – Fully Funded
PhD position summary/title: PhD in Adaptive Connectivity Architectures for Cyber-Physical Systems
Modern cyber-physical systems (CPS), such as semiconductor manufacturing machines and advanced mechatronic systems, rely on ultra-reliable, low-latency, and deterministic communication between distributed sensors, controllers, and actuators. Ensuring such performance under dynamic workloads, strict real-time constraints, and limited computational resources remains a key technical challenge. This PhD project addresses these challenges by developing efficient optimization methods for run-time network configuration and control. You will design efficient and lightweight learning-based techniques for automated scheduling, network resource allocation, and parameter setting, enabling fast and predictable adaptation with minimal overhead. A central focus is the co-design of algorithms with edge hardware and embedded platforms. You will investigate implementation strategies that account for limited compute, timing constraints, and energy efficiency, bridging theory and deployable solutions. This includes integration of wired and wireless communication technologies and support for run-time adaptation and fault resilience. The developed concepts will be validated through prototyping and experimental evaluation in realistic setups or digital twins, with performance assessed in terms of latency, reliability, and scalability, in close collaboration with academic and industrial partners.
Deadline : 15-05-2026
(04) PhD Degree – Fully Funded
PhD position summary/title: PhD in Addressing rebound effects in home automation through alternative design aesthetics
The aim of this exciting, creative, critical and multidisciplinary project is to develop design strategies to anticipate and prevent ‘rebound effects’ in the smart home by exploring the potential of aesthetics of uncertainty, instability and emergence. These strategies should evoke the imagination and competence of designers to challenge currently dominant paradigms in home automation and form the basis for developing novel Key Enabling Methodologies.
Deadline : 05-05-2026
(05) PhD Degree – Fully Funded
PhD position summary/title: PhD in AI-Enabled Digital Twins and Inclusive Neighborhood Design
Designing inclusive neighborhood environments requires tools that translate insights about residents’ needs into concrete spatial and technological interventions.
This PhD position is part of the ELLI project (Empathic Living Labs for Inclusive Neighborhoods), an interdisciplinary initiative exploring how technology and urban design can create neighborhoods that support autonomy, accessibility, and participation for older adults. The project focuses on the development of AI-enabled design tools and digital twin models that support the creation of inclusive neighborhood interventions.
The candidate will develop computational methods that integrate spatial data, environmental sensing, and behavioral insights into digital representations of neighborhoods. These digital twins will support the design and testing of interventions addressing physical, cognitive, social, and psychological accessibility.
The research will involve computational modelling, AI-enabled analysis, and participatory design processes with residents and stakeholders in the living lab neighborhoods.
The goal is to develop design methods and digital tools that support municipalities, designers, and planners in creating inclusive neighborhood environments.
Deadline : 20-05-2026
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(06) PhD Degree – Fully Funded
PhD position summary/title: PhD in Algorithms and Complexity for Discrete Optimization Problems
This project focuses on investigating theoretical properties and the computational hardness of discrete optimization problems, with a focus on structured cases in problems from operations research such as scheduling and integer programming. While many of these problems are classically NP-hard, specific formulations often have exploitable structures that enable efficient solution methods. This research aims to identify such tractable structures, develop corresponding algorithms, and sharpen the boundaries between polynomial-time solvability and inherent hardness.
Beyond theoretical insights, the project seeks to translate these advances into practical algorithmic frameworks for combinatorial optimization. Improvements in solving structured discrete optimization models have wide-ranging implications across applications including supply chain management, resource allocation, fair division and voting rules, among others.
Deadline : 31-05-2026
(07) PhD Degree – Fully Funded
PhD position summary/title: PhD in Behavioral and Experiential Modeling of Inclusive Neighborhood Environments
Neighborhood environments may contain physical, cognitive, social, and psychological barriers that affect how older adults move through, experience, and use everyday environments. However, many of these barriers remain insufficiently understood.
This PhD position is part of the ELLI project (Empathic Living Labs for Inclusive Neighborhoods), a national interdisciplinary initiative investigating how neighborhood environments shape the everyday experiences, behavior, mobility, and accessibility of older adults with physical or cognitive vulnerabilities. The project focuses on understanding how older adults experience, interpret, and navigate neighborhood environments, and how these experiences relate to everyday mobility, environmental interaction, and accessibility.
The candidate will investigate behavioral patterns, environmental perception, and everyday mobility in neighborhood settings. The goal is to develop behavioral and spatial models that reveal implicit accessibility barriers and inform neighborhood-level intervention strategies.
The research will combine qualitative, quantitative, and spatial methods, including behavioral observation, environmental perception studies, interviews, wearable and sensor-based data collection, spatial behavior modelling, GIS and spatial analytics, and participatory approaches.
The insights generated in this project will provide a scientific foundation for the analysis and development of neighborhood interventions within the ELLI project.
Deadline : 20-05-2026
(08) PhD Degree – Fully Funded
PhD position summary/title: PhD in De novo design of ice-binding proteins
Ice-binding proteins (IBPs) protect against freeze damage in polar regions by keeping the nucleation and growth of ice crystals in check. How this works exactly is not yet known. You will engineer IBPs de novo, express, purify, and study these to elucidate the relation between IBP structure and function and explore the application potential of IBPs as cryoprotectants. In this project you will combine computational tools for protein engineering with biochemical and structural biology techniques to engineer thermostable IBPs with tailored activity. Particularly, you will focus on IBPs that inhibit ice recrystallization and control nucleation for biomedical applications in e.g. heart and kidney cryopreservation.
Deadline : 06-05-2026
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(09) PhD Degree – Fully Funded
PhD position summary/title: PhD in Entanglement Generation and Routing in Multi-Node Quantum Networks
Efficient distribution and routing of entanglement are indispensable for quantum‑secure communication (e.g., advanced QKD networks), distributed quantum computing, quantum‑secure enterprise connectivity and quantum teleportation services across metropolitan or regional distances. However, unique features of quantum information, such as non-amplification, no-cloning, or measurement without altering the quantum states, make it difficult to re-use the architecture and protocols of classical telecom networks for quantum communication and networking. In contrast to classical optical signals, quantum states are extremely fragile and degrade rapidly due to loss, noise, and decoherence, especially over long distances. As a result, conventional telecom solutions such as optical amplification, regeneration, and routing cannot be directly applied to quantum networks. Nevertheless, recent advances in quantum state generation and measurement have opened new opportunities for the development of a quantum network architecture and protocols for routing and teleportation as a critical enabler for quantum networks.
In this PhD project, you will investigate the time-bin entanglement generation and detection, architecture, and protocols of entanglement routing in a multi-node quantum network. Additionally, you will investigate the coexistence of classical telecom traffic and quantum signals, effect of impairments, such noise, crosstalk, etc. The project will combine theoretical and simulation studies and validate them with experimental studies. The project will leverage KPN’s expertise to address real‑world constraints—including scalability, interoperability, reliability, and manageability—while developing the telecom‑grade quantum networks.
The PhD position is fully funded by our industry partner (KPN) in Smart Optical Networks Lab (SONL) of the Electro-Optical Communication group. The research activity of SONL covers a wide range of topics, including network design and software control, reconfigurable interconnects for telecom, datacenters, AI clusters, and quantum networks, down to heterogeneous photonic integrated circuits and systems for enabling low-latency, fast network reconfiguration in real systems. Additionally, we focus on innovative solutions for switching and routing in secure quantum-optical networks. The ECO group covers a wide range of topics from the Photonic layer to the Application layer of the communication system, and we also play a key role in the Casimir Institute at TU/e, and within the PhotonDelta and QuantumDelta growth fund program. Besides research you will also contribute to education within our group and the Department of Electrical Engineering and mentor BSc and MSc students during their research projects.
Deadline : 23-05-2026
(10) PhD Degree – Fully Funded
PhD position summary/title: PhD in Femtosecond optical approaches to magnetic topology on demand
Topologically-protected conductors of information are robust against perturbations that would normally scatter the information carriers, resulting in waste heat and reduction of energy efficiency. In addition to topological protection of electronic charge transport, such protection of propagating fluctuations of the magnetization – dubbed spin waves or magnons – has recently been gaining attention. This PhD project will focus on using femtosecond laser pulses to create (periodic) assemblies of nanosized chiral spin textures on demand. These tunable structures are predicted to host topological spin waves, and will provide a unique playground to experimentally explore their fascinating dynamics.
This project consists of an experiment-theory collaboration in which a PhD candidate in Experimental Nano & Ultrafast Magnetism, supervised by Prof. Bert Koopmans, based at Eindhoven University of Technology, collaborates closely with a PhD candidate in Condensed-Matter Theory based at Utrecht University, supervised by Prof. Rembert Duine.
In this project, we aim to develop an optical pen using laser-induced magnetization dynamics and switching to write topological magnetic periodic structures that consist for example of skyrmions, topological magnetic whirls in the magnetization. Along the edges of such structures, topologically-protected spin waves will propagate without scattering and loss of energy. Such structures could potentially be a novel building block for future computer chips and more sustainable IT technologies.
Your research will be to design and fabricate magnetic thin films using the NanoAccess facility, contribute to the development of the ‘optical pen’ to write patterns of skyrmions, and explore their dynamics with a suite of state-of-the-art time and spatially resolved optical and electrical tools. Research will include femtosecond magneto-optical spectroscopies, as well as magnetic force- and scanning electron microscopy.
Deadline : 08-05-2026
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(11) PhD Degree – Fully Funded
PhD position summary/title: PhD in Imaging ice-interactive biomaterials
The formation and growth of ice during freezing, storage, and/or thawing of cryopreserved materials limits the potential of cryopreservation technology for the long-term storage of sensitive materials, such as cells, tissues, and organs. Our group aims to unravel how natural and synthetic ice-interactive materials function so that we can create customized chemical cryoprotectants for biomedical and other applications. Recently, we reported sub-zero super-resolution microscopy experiments which allowed us to study for the first time the interfacial dynamics of ice-bound ice-binding proteins at the single molecule level (see PNAS publication for more information). In this project you will build upon these pioneering experiments. You will image the interaction with ice of a novel class of engineered ice-interactive materials including proteins at the single molecule level to advance our understanding of how their structure and properties impact their ability to modulate ice crystal formation and growth. In this exciting role, you will work in a vibrant and international research group aiming to advance molecular insight into the working mechanism of novel ice-interactive materials for biomedical applications.
Deadline : 06-05-2026
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(12) PhD Degree – Fully Funded
PhD position summary/title: PhD in Low-Energy Manufacturing of Silicon Carbide | TU/e & DTU
Silicon carbide (SiC) is one of the most remarkable engineering materials — extraordinarily hard, thermally stable, chemically resistant — yet notoriously difficult and energy-intensive to produce. This PhD project sets out to change that, by pioneering an entirely new, low-energy manufacturing route for SiC. You will combine advanced sintering techniques, materials characterization, and optimization tools to push the boundaries of what is possible in ceramic manufacturing.
Deadline : 26-05-2026
(13) PhD Degree – Fully Funded
PhD position summary/title: PhD in Mining Simulation Models for Circular Supply Chain Processes
We are seeking a highly motivated research assistant to develop innovative methods and tools for process mining and supply chain simulation, culminating in a PhD. This position is part of the STRIDE project (Strategic Design for Circular High-Tech Manufacturing), which aims to embed circularity in both product design and supply chain design for high-tech systems. STRIDE consists of three interconnected work packages: circular product design and multi-criteria evaluation (WP1), circular supply chain network design (WP2), and data-driven simulation of designs (WP3, this position), which together enable integrated decision-making for circular high-tech manufacturing. For the position in WP3, the candidate will
- Innovate and Develop: Create novel methods and tools for mining supply chain processes from data.
- Collaborate with Industry Leaders: Work closely with ASML, Neways, Prodrive, KMWE, and Eriks to understand their circular supply chain, understand the data they have on supply chain process execution, mine supply chain simulation models based on that data, and validate your methods, tools, and simulation models in real-world settings.
- Design for Collaboration: Develop shared modeling and simulation frameworks and visual decision-support environments that enable supply chain partners to collaboratively explore network configurations, responsibility allocations, and circular flow strategies (e.g., repair vs. replace, reverse logistics) based on real-world data.
- Engage with the Research Community: Collaborate with university colleagues and the international research community, discussing your findings and insights on the (inter)national stage.
- Present Your Work: Report your developments at prestigious international conferences, in leading journals, and at industry events.
- Achieve Academic Excellence: Contribute to the field through your research and ultimately write a PhD thesis.
This position offers a unique opportunity to work on impactful projects that bridge the gap between circularity ambitions and day-to-day supply chain realities, develop your skills in both fundamental research and practical application, and contribute to advancements in circular supply chain design. If you are driven by innovation and eager to make a difference in the transition toward a circular economy, we encourage you to apply.
Deadline : 22-05-2026
(14) PhD Degree – Fully Funded
PhD position summary/title: PhD in Pioneering Photonic Terahertz Sensors for Fusion Energy
Fusion energy is one of the most promising solutions to the global energy challenge, offering a clean, safe, and virtually limitless source of power. However, controlling plasma instabilities in fusion reactors remains a critical hurdle. Advanced diagnostics are essential to achieve stable and efficient operation.
In this PhD project, you will design and develop integrated photonic terahertz sensors for Electron Cyclotron Emission (ECE) measurements in fusion reactors. These sensors will enable broadband, high-resolution diagnostics, replacing bulky RF systems with compact, photonic-based solutions. Your work will involve:
- Architecting and simulating photonic-based radiometer systems
- Designing and fabricating photonic integrated circuits (PICs) for terahertz sensing
- Building and testing prototypes, from commercial-off-the-shelf components to fully integrated PIC-based systems
- Validating the technology in collaboration with the Max Planck Institute for Plasma Physics (IPP) on the Wendelstein 7-X fusion reactor.
You will be embedded in the Quantum and Terahertz Systems (QTS) group of the Department of Electrical Engineering, and the Science and Technology of Nuclear Fusion group of the Department of Applied Physics. Furthermore, you will collaborate with leading partners in photonics and fusion research, including SMART Photonics and IPP. This position is part of the Photofusion project, funded by NWO and supported by the National Growth Fund initiative PhotonDelta.
Deadline : 08-05-2026
(15) PhD Degree – Fully Funded
PhD position summary/title: PhD in Statistical Bioinformatics
Statistical modelling of multiple omics datasets is a lively and rapidly developing research area. Ongoing technological advances make it possible to measure multiple biological data types that reflect related biological mechanisms, such as proteomics, metabolomics, and transcriptomics. Latent variable models are widely used for identifying the underlying structure in such high-dimensional data and for dimension reduction.
Recent methodological developments have extended probabilistic principal component models to settings involving multiple datasets, for datasets spanning biobanks, and for longitudinal designs. Some of these latent models capture also dataset‑specific components, account for heterogeneity of the joint components across datasets, or incorporate covariates. However, comparable flexible models for longitudinal data are still lacking. Moreover, it remains unclear how to assess whether simpler latent variable models adequately fit these complex datasets.
During this PhD project, you will develop methods to assess goodness-of-fit for latent variable models for multiple omics datasets and develop flexible models for longitudinal designs. You will apply your approaches to several available omics datasets, both open-source and provided by collaborators, and you will develop R packages to implement the methods. By improving model assessment and interpretability, your work will contribute to deeper insights into biological pathways and disease mechanisms, ultimately supporting advances in diagnosis, treatment selection, and monitoring in healthcare. You will present your work to the statistical community and to collaborators from applied domains.
Deadline : 17-05-2026
(16) PhD Degree – Fully Funded
PhD position summary/title: PhD in Visual Analytics for Historical Paintings
Paintings are examined to understand artistic processes, support conservation, and aid authentication. Modern scientific imaging techniques now generate large volumes of complex data that can reveal new insights for art historians, conservators, and the broader public. Among these techniques, X-ray fluorescence (XRF) imaging provides detailed information on the distribution of chemical elements across paint layers, giving unique access to pigments, mixtures, and underlying compositions. The resulting datasets are high‑dimensional and spatially rich, posing significant analytical challenges.
The generation of the high-dimensional images themselves follows a complex pre-processing from the raw scans usually to chemical components. Information is lost and uncertainty is added in the process.
Visual analytics offers promising ways to interpret such high‑dimensional imaging (HDI) data, yet dedicated methodologies tailored to paintings remain underdeveloped. This PhD project aims to create interactive visual analysis frameworks and methods that enable reliable, interpretable exploration of multimodal painting data, with a strong focus on XRF‑derived HDI. The project will deliver new visual analytics techniques and prototype tools that support the study of pictorial artworks.
It is expected that the candidate will author high-quality scientific papers and showcase outputs of this work at international conference. The candidate will also implement open-source software prototypes to demonstrate the effectiveness of the proposed methods.
This position is part of a collaboration between the Visualization Cluster (https://research.tue.nl/en/organisations/visualization-3/) at Eindhoven University of Technology (TU/e) and the Van Gogh Museum. TU/e provides leading expertise in visual analytics, scientific visualization, Explainable AI and the analysis of complex imaging datasets. It has generated several award winning contributions at major visualization conferences (IEEE VIS, IEEE InfoVis, IEEE VAST, EuroVis). The Van Gogh Museum, one of the world’s foremost cultural heritage institutions, increasingly acquires such data in its research on the Van Gogh collection. This partnership offers a unique opportunity to contribute to cutting‑edge digital heritage research at the intersection of science, technology, and the arts.
Deadline : 15-05-2026
(17) PhD Degree – Fully Funded
PhD position summary/title: PhD on AI-Driven Lifestyle Coaching for People with Severe Mental Illnesses
In the Netherlands, almost 300.000 people suffer from severe mental illness (SMI, e.g., bipolar disorder, recurrent major depressions), which has major impacts on their life quality. Adopting healthy lifestyle behaviors, like physical activity, nutrition, sleep, and stress management, could help them a lot and complement psychological and pharmacological treatments. However, while lifestyle coaching is already included in care guidelines, its implementation remains too demanding and costly for professionals. To address this challenge, the THRIVE project aims to develop a personalized AI-driven tailored lifestyle coaching solution that is accessible via users’ own smartphones and that delivers just-in-time guidance integrated in their daily lives.
To realize this goal, we invite highly motivated candidates with a background in human-computer interaction (HCI), computer science, AI, or design to apply for a PhD position in the Department of Industrial Design at Eindhoven University of Technology (TU/e). You will work together with academic, industrial, and societal partners across the Netherlands to realize this ambitious project. At Eindhoven University of Technology, you will be supervised by Sebastian Cmentowski, Pieter van Gorp, and Panos Markopoulos. Additionally, your research will be conducted at and guided by TNO Health & Work in Leiden under supervision of Hilde van Keulen and Pepijn van Empelen.
Based on the needs of people with SMI, you will establish a data collection infrastructure using ecological momentary assessments, digital diaries, and wearables to collect mental-health influencing factors. Therefore, you will extend existing state-of-the-art tools and AI libraries developed at the TU/e, such as GameBus and Experiencer. The collected data forms the basis for developing predictive AI models that tailor coaching content and timing to individual needs. On the frontend, you will further develop a lifestyle coaching system that follows established behavior change and gamification strategies and that uses the developed models to create a conversational interface to adapt to individual’s goals and needs. The developed intervention will finally be evaluated in a feasibility study with help from our clinical partners and prepared for integration within care practice.
Deadline : 31-05-2026
(18) PhD Degree – Fully Funded
PhD position summary/title: PhD on Bimanual Robotic Manipulation in the Open World
The demand for autonomous robots capable of physically interacting with the world in flexible and adaptable ways is rapidly increasing across industries and society. These robots are needed to perform complex and fast physical interaction tasks, spanning from fine manipulation, such as kitting and cables manipulation, to heavy non-ergonomic tasks in semi-structured environments, such as depalletization and truck unloading.
This PhD project wants to explore the latest advances in machine learning, physics simulation, online robot control, and robot hardware to generate robust contact-rich strategies that allow to perform bimanual manipulation tasks in man-made environments, such as handling of parcels, bags or clothes, targeting industry relevant use cases at required execution speed. This is enabled by latest tactile robots that are back-drivable and capable of sensing contact interactions with the environment as well as computational hardware, machine learning methods, and parallel physics simulation software, enabling to train complex control policies or adaptable sampling-based MPC strategies.
Key Objectives and Challenges of this PhD Position Include:
- Develop machine learning models, e.g., vision-action models, to manipulate parcels, bags, and clothes in clutter, allowing to adapt to object properties on the fly, while also continuously monitor task execution, swiftly replanning in case of inevitable occasional failures
- Collect experimental data and make it available according to FAIR principles and, where relevant, use it to validate physics engines (such as, e.g., Isaac Sim, MuJoCo, Algoryx Dynamics) against real experiments, to explore the sim2real limits and use it to propose control strategies that respect and exploit the natural robot-environment contact dynamics for boosting task success rate
- Perform experimental work on the various robotic manipulation platforms available in the lab to assess progress with respect to the state of the art and showcase results to our research and industrial network
Deadline : 22-05-2026
(19) PhD Degree – Fully Funded
PhD position summary/title: PhD on Numerical Modelling of Subsurface Erosion in Dikes and Geothermal Wells
In this PhD position, you contribute to a high-impact research project that combines advanced modelling with lab-scale experimentation to better understand and predict subsurface erosion phenomena.
Subsurface erosion—often invisible until it causes catastrophic damage—poses major risks to buried infrastructure. In this PhD project, you will investigate two pressing real-world challenges: sand production in geothermal boreholes and backward erosion piping in dikes. Both processes are driven by internal erosion mechanisms, which will be studied by developing and extending coupled hydro-mechanical erosion models.
This research simulates subsurface erosion in geothermal wells and dikes with advanced finite element models calibrated through small- and medium-scale laboratory experiments. It investigates the effects of soil properties, flow paths, and scale, aiming to develop practical prediction tools for engineering applications.
You will be part of the chairs of Applied Mechanics and Engineering Thermodynamics for Energy Systems at the Eindhoven University of Technology (TU/e), Netherlands. You will work closely with experts in numerical simulations, soil mechanics and geotechnical modelling. Your work will help safeguard infrastructure, improve geothermal energy systems, and enhance flood protection strategies—contributing to climate-resilient, sustainable development. You will be part of a supportive, inclusive, and multidisciplinary team tackling real-world problems with global relevance.
This project will be embedded within the BEHeaT program on subsurface heat extraction of the Eindhoven Institute for Renewable Energy Systems (EIRES) of the TU/e, serving as a valuable addition to the suite of methodologies for sustainable heat generation. Approaching the topic from a systems perspective within the broader BEHeaT framework, and linking related efforts from the Built Environment and Mechanical Engineering departments, the project strengthens interdepartmental collaboration and aligns well with EIRES’s energy transition goals.
Deadline : 31-05-2026
(20) PhD Degree – Fully Funded
PhD position summary/title: PhD on SecReSy4You: Hybrid Physics-ML Anomaly Detection, Risk Assessment, and Attack-Tolerant Control with Probabilistic Guarantees for Networked Cyber-Physical Systems (NCPSs)
The Dynamics and Control group at Eindhoven University of Technology (TU/e) conducts world-class research aligned with the technological interests of the high-tech industry in the Netherlands, with a focus on the Brainport region. Our goal is to produce engineers who are both scientifically educated and application-driven by providing a balanced education and research program that combines fundamental and application aspects. We equip our graduates with practical and theoretical expertise, preparing them optimally for future challenges.
This PhD position is part of the SecReSy4You MSCA Doctoral Network, which focuses on developing next-generation methods for security and resilience of cyber-physical systems (CPSs), with emphasis on resilient control, learning-enabled systems, and system-level assurance under adversarial conditions.
The SecReSy4You network brings together 10 doctoral candidates across leading European universities and industrial partners, forming a highly collaborative and interdisciplinary research environment. As part of the program, the PhD candidate will engage in international secondments with partner institutions and companies, gaining exposure to both academic and industrial settings. The network also offers a rich program of training schools, workshops, and demonstrators, providing opportunities to develop technical expertise, build a professional network, and contribute to cutting-edge applications in cyber-physical systems security.
Within this framework, the PhD candidate will contribute to Work Packages 2 and 3, focusing on the development of data-driven and learning-informed control methodologies for networked cyber-physical systems (NCPSs).
Modern CPSs—such as industrial automation systems, autonomous platforms, and critical infrastructure—are increasingly exposed to cyber-physical attacks and uncertainties. These disturbances induce complex, time-evolving performance degradation that requires tightly integrated approaches combining control, learning, and uncertainty quantification.
Deadline : 08-05-2026
(21) PhD Degree – Fully Funded
PhD position summary/title: PhD position on Experimental Study on Combustion of Impure Iron Powders for Sustainable Energy
Eindhoven University of Technology (TU/e) invites applications for an experimental PhD position in the Department of Mechanical Engineering. This position is part of a newly funded research project within the Open Technology Programme (OTP) of the Dutch Research Council (NWO), focusing on the Iron Power Cycle as a circular, carbon-free energy carrier.
The PhD candidate will investigate the combustion behavior of iron powders containing controlled impurities, aiming to enable the use of low-cost, industrially sourced iron powders for large-scale energy storage and transport. The project combines advanced combustion experiments with materials characterization, and works in close interaction with complementary multiscale modeling activities carried out by collaborating researchers.
You will conduct single-particle combustion experiments, solid-phase oxidation studies, and in-depth post-combustion material analyses, contributing to a fundamental understanding of how impurities such as carbon and metal oxides affect ignition, combustion temperature, and particle morphology. Your work will directly support the development of predictive models for next-generation metal-fuel energy systems.
Deadline : 26-05-2026
About Eindhoven University of Technology, Netherlands –Official Website
The Eindhoven University of Technology is a public technical university in the Netherlands, located in the city of Eindhoven.
The University has been placed in the top 200 universities in the world by three major ranking tables. The 2019 QS World University Rankings place Eindhoven 99th in the world, 34th in Europe, and 3rd in the Netherlands – TU/e has moved up 59 places in this world ranking since 2012 (in two other main world rankings it is 167th and 51-75th). As of 2020, the foundation employs over 800 people, with annual revenues in excess of €686 million.
TU/e is the Dutch member of the EuroTech Universities Alliance, a strategic partnership of universities of science & technology in Europe: Technical University of Denmark (DTU), École Polytechnique Fédérale de Lausanne (EPFL), École Polytechnique (L’X), The Technion, Eindhoven University of Technology (TU/e), and Technical University of Munich (TUM).
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