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 Catalytic Upgrading of Lignin-Derived Phenolics to Aromatics
Lignin valorization is a key challenge in the transition towards a circular and sustainable chemical industry. Depolymerized lignin streams contain complex mixtures of phenolic compounds that can serve as valuable feedstocks for specialty chemicals, yet efficient catalytic upgrading routes are still underdeveloped.
In this project, you will develop catalytic processes to convert lignin-derived phenolics into valuable aromatics and chemical building blocks. The research focuses on the selective demethoxylation and trans-alkylation of alkylmethoxyphenols using bifunctional catalysts.
You will perform catalytic experiments in continuous-flow reactors to evaluate activity, selectivity, and stability under realistic process conditions, and analyze both liquid- and gas-phase products to understand reaction pathways and overall process efficiency. Using a combination of model compounds and real lignin-derived feeds, you will investigate the influence of feed composition and operating conditions and apply systematic optimization strategies to maximize yields of desired products.
A key aspect of the project is understanding and improving catalyst stability, including identifying deactivation mechanisms and developing strategies for regeneration or improved catalyst design. The work will be carried out in close collaboration with academic and industrial partners within the consortium, ensuring strong alignment with upstream lignin processing and downstream application of the produced chemicals.
Deadline : 15-08-2026
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(03) 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 : 17-07-2026
(04) PhD Degree – Fully Funded
PhD position summary/title: PhD Humanoid robot design
We invite highly motivated students with a strong background in the modelling and design of dynamical systems and a keen interest in humanoid robotics to apply for this PhD position within the Robotics section at the Department of Mechanical Engineering, Eindhoven University of Technology.
In biological systems, high actuator redundancy and intrinsic compliance are essential for achieving human-like interaction with their environment. Translating these principles to humanoid robots remains a major scientific and engineering challenge. The aim of this PhD project is to explore novel compliant and muscle-inspired actuation concepts, as well as actuator arrangements that enable more human-like behaviour and enhanced manipulation capabilities.
To support this investigation, you will develop an advanced simulation framework that captures the coupled interaction between robot mechanics, actuator dynamics, and state-of-the-art whole-body control. These tools will enable systematic exploration and optimization of humanoid actuator architectures, bridging the gap between actuator design and whole-body robot performance.
Deadline : 11-07-2026
(05) PhD Degree – Fully Funded
PhD position summary/title: PhD in Active Inference for Manufacturing Processes
In manufacturing, uncertainties may arise from stochastic machine behavior, sensory noise, changes in environment/context and incomplete information. Real-time adaptation to such disturbances is crucial to the efficiency and effectiveness of the manufacturing process. Active inference is a first-principles theory about how agents act and adapt under uncertainty.
By combining active inference principles with probabilistic graphical modeling, event knowledge graphs and physics-informed neural nets, you will enable agents to reason under uncertainty, test hypothesis and adjust internal models based on observed data. These techniques should provide full decision traceability, ensuring explainability and accountability of developed agents for application in complex manufacturing processes.
This PhD project is funded by the Horizon Europe program (https://tinyurl.com/mr7wjukv). You will work in the BIASlab team (http://biaslab.org) in the Electrical Engineering department at TU/e. This lab focuses its research activities on transferring Active Inference principles to practical use in industrial contexts. Please see this youtube presentation (https://youtu.be/QYbcm6G_wsk) for more information about our research. During this project you will closely collaborate with other BIASlab researchers, as well as with project team members in the Process Analytics Cluster (https://tinyurl.com/3uee5xt7), and with our industrial partners.
An important part of the PhD research will be devoted to contributing to RxInfer (https://rxinfer.com/), which is a toolbox-under-development for automating real-time Bayesian inference. Hence, your work will partly consist of developing and coding fundamental (Bayesian) AI tools, and partly on applying these tools to manufacturing applications.
Deadline : 11-07-2026
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(06) PhD Degree – Fully Funded
PhD position summary/title: PhD in Adapting Transformer Models for Defect Detection with Limited Data
Industrial edge deployments—in semiconductor manufacturing, industrial printing systems, automotive radar, smart mobility cameras, and HealthTech—require on-device AI to ensure low latency, privacy, and resilience. Today’s Transformers models scale poorly and assume abundant cloud resources. The research program FIND aims to deliver architectural and algorithmic breakthroughs that enable foundation models to run predictably and efficiently on embedded processors and accelerators.
FIND is a research program funded by the Dutch government and industry that brings together 5 universities, 11 companies (startups to multinationals), and 2 knowledge institutes to develop foundation models (large AI models) for Dutch high‑tech industry, with strong emphasis on edge deployment, privacy, and timely decision‑making. Partners include ASML, NXP, Canon Production Printing, ASMPT, Technolution, Signify, Shell, Stryker, TNO, and others. A total of 12 PhDs will be employed on the FIND program covering topics from foundation model pre-training and multimodal adaptation to architectures and compression for edge deployment while targeting real-world validation in domains like HealthTech, smart industry, and autonomous mobility.
This PhD position focuses on adapting and fine-tuning Transformer-based foundation models for defect detection in high-tech manufacturing environments where only limited and largely unlabeled defect data is available. Current solutions typically rely on supervised CNN-based models trained on large labeled datasets, which fail when defects are rare, vary across machines, or when labeling is prohibitively expensive. These approaches lack flexibility and generalization, making them unsuitable for dynamic industrial settings with scarce and imbalanced data.
You will also explore few-shot learning, self-supervised adaptation, and multimodal integration techniques to overcome data scarcity and improve robustness. Unlike existing methods that depend on exhaustive annotation or handcrafted features, this research will leverage the rich representations of foundation models and develop strategies for zero-shot or few-shot adaptation. You will investigate domain adaptation, synthetic data generation, and cross-modal learning to enable models that generalize across defect types and machine configurations. This ensures scalable, accurate defect detection even in low-resource industrial contexts.
The resulting models will be validated in collaboration with a lead high-tech company, demonstrating how foundation models can transform quality inspection by reducing dependency on labeled data and enabling rapid adaptation to new defect patterns—closing the gap between AI capability and real-world manufacturing constraints.
Deadline : 24-07-2026
(07) PhD Degree – Fully Funded
PhD position summary/title: PhD in Advanced in vitro modeling and biomechanical characterization of intravasation
Cancer still is one of the world’s deadliest diseases. Most devastating is the ability of cancer cells to spread throughout the body in a multistep process called metastasis, the major cause for mortality. A critical step is intravasation, when cancer cells pass a blood vessel wall to enter the blood stream, and travel to another location to create a secondary tumor. However, the mechanisms of intravasation are still largely unexplored, even though it is pivotal: if we can prevent it, cancer does not spread. In the ERC-AdG research project “Intrap”, we aim to enhance our understanding of intravasation through a unique experimental approach.
We hypothesize that intravasation is critically controlled by mechanical cues like stiffness, geometry, and adhesive interactions; after all, the act of passing the vessel wall requires mechanical action of the cells: deforming, adhering, detaching, moving, and overcoming mechanical forces. We will take a systematic approach enabling to separately control different mechanical cues, and directly observe their effect on intravasation.
We are developing an innovative microfluidic platform with a multi-chip architecture for in vitro intravasation testing. In the individual chips we create blood vessels with varying geometry and permeability, surrounded by extracellular matrices (ECM) with pre-determined mechanical properties in which different tumor cells or cell clusters with known or controlled mechanical properties are integrated. We will directly observe the intravasation process using live microscopy. Our platform will open new approaches to cancer research; our discoveries will expand our scientific knowledge of metastasis. Beyond Intrap, the obtained insights can contribute to the development of highly needed novel cancer treatments that intervene with mechanical properties of cancer cells, matrix or blood vessels.
Deadline : 15-07-2026
(08) PhD Degree – Fully Funded
PhD position summary/title: PhD in AI for reconfigurable robot collectives and real-time flow control
You will develop the intelligence layer of the RE-FLOW architecture, developing new closed-loop flow-shaping approaches to answer a central research question:
How can high-dimensional, emergent crowd dynamics and hazard information be translated into optimal and safe geometric configurations of a robot collective in real time?
Rather than controlling individual robots, you will investigate how high-level geometric structures—such as corridors, barriers, and splitters—can be synthesized automatically from observations of pedestrian flow, enabling the robot collective to act as a form of adaptive physical infrastructure that continuously reshapes itself in response to evolving crowd dynamics, hazards, and environmental changes.
Under the joint supervision of Valentina Breschi (Department of Electrical Engineering), Elena Torta (Department of Mechanical Engineering), and Federico Toschi (Department of Applied Physics and Science Education), you will develop novel methods to:
- Extract actionable information from crowd-flow observations, such as congestion patterns, bottlenecks, and indicators of reactions to emerging hazards.
- Design learning-based policies that generate safe, high-level geometric configurations for robot collectives accounting for both flow performance and physical feasibility of collective reconfiguration.
- Validate the developed methods in simulation and on TU/e’s RoboFlow robotic testbed.
This project offers a unique opportunity to work on a new research direction at the intersection of control, AI, and robotics. You will collaborate with researchers from Electrical Engineering, Computer Science, Applied Physics, and Mechanical Engineering and become part of the vibrant AI ecosystem of TU/e and the Eindhoven Artificial Intelligence Systems Institute (EAISI).
Deadline : 11-07-2026
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(09) PhD Degree – Fully Funded
PhD position summary/title: PhD in AI-driven Fair Energy Curtailment Policies
The rapid growth of Distributed Energy Resources (DERs), such as rooftop photovoltaics (PV) is transforming electricity distribution networks. While essential for decarbonization, this transition creates new operational challenges. Local electricity networks can become congested when many producers inject power simultaneously or due to electrification of consumption, such as heat pumps (HPs) and electric vehicles (EVs). This project aims to develop fair and reliable AI-based control methods for managing congestion in electricity distribution networks. The core idea is to embed clear and interpretable notions of fairness directly into the algorithms that determine how much energy different prosumers may inject during congestion.
The proposed project aims to advance fundamental science at the intersection of electrical power engineering, control, machine learning, and sociotechnical design. It seeks to redefine how societal and economic objectives are formalized and enforced in intelligent control systems. Achieving this requires close interdisciplinary collaboration.
You will be embedded in the Control Systems group at TU/e, contributing to its research program on intelligent and responsible control of networked systems.
You will collaborate closely with experts in responsible AI, energy systems, and distributed control, working across departments in an interdisciplinary team.
Deadline : 08-07-2026
(10) PhD Degree – Fully Funded
PhD position summary/title: PhD in Atomic layer deposition for novel battery materials
Within the Plasma & Materials Processing group at Eindhoven University of Technology (TU/e), you will contribute to advance our growing research efforts in Atomic Scale Processing for next-generation energy devices. The central challenge of the research team is to design and engineer interfaces at the atomic scale by means of atomic layer deposition, for green hydrogen production, highly efficient metal halide perovskite-based photovoltaics and, of course, high energy density and safer batteries for e-mobility .
You will work in a highly collaborative environment within the Plasma & Materials Processing group, embedded in TU/e’s strong Brainport ecosystem, as well as tightly connected to other academic partners and industry through national research consortia (https://www.batterynl.nl/ and nanexbat.nl/). You are expected to contribute to scientific output, supervise Bachelor and MSc students, and actively collaborate with academic and industrial partners.
Deadline : 29-07-2026
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(11) PhD Degree – Fully Funded
PhD position summary/title: PhD in Chemical and Neurogenic Haptics Using Responsive Polymer Coatings
We are looking for an enthusiastic PhD candidate to develop chemical and neurogenic haptic interfaces based on responsive liquid crystal polymer coatings. This position is part of a Vici project entitled “Beyond sight and sound: liquid crystal polymer-based tactile interfaces for the digital age.” The project aims to create tactile technologies that go beyond conventional vibrations by generating programmable sensations such as texture, softness, adhesion, warmth, coolness, and chemical cues.
In this PhD project, you will focus on the controlled release and reabsorption of active compounds from porous liquid crystal polymer coatings. These coatings will act as dynamic reservoirs that can release molecules on demand when triggered by light, heat, or electric fields. Target sensations include cooling, warming, burning, fragrance, and other chemosensory effects relevant to human-machine interaction.
The project will explore compounds such as menthol, capsaicin, methyl salicylate, and volatile molecules, while developing material strategies to prevent premature release and enable repeatable, localized, and safe stimulation. In the later stage of the PhD, you will combine chemical haptics with mechanical haptic functions such as topography changes, fibres, or suction-inspired structures.
Deadline :01-08-2026
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(12) PhD Degree – Fully Funded
PhD position summary/title: PhD in Credible hybrid AI-mechanistic models for clinical decision-making: unveiling the untapped potential of sensitivity analysis methods
At the Department of Biomedical Engineering at Eindhoven University of Technology (TU/e), we offer a fully funded 4-year PhD position within the project HY-Credibility – Credible hybrid AI–mechanistic models for clinical decision-making: unveiling the untapped potential of sensitivity analysis methods. You will be embedded in the Cardiovascular Biomechanics group led by Prof. Huberts and co-supervised by dr. Jemima Tabeart of the Computational Science group, Centre for Analysis, Scientific Computing and Applications (Department of Mathematics & Computer Science).
Your challenge: Extracorporeal membrane oxygenation (ECMO) is a life-saving therapy, but deciding when and how to safely wean patients from cardio-respiratory support remains a major clinical challenge. In this project, you will develop credible hybrid digital twins that combine first-principles cardiovascular pathophysiology with data-driven AI. A key innovation is the use of sensitivity analysis (SA) and uncertainty quantification (UQ) to make these models transparent, testable, and trustworthy for clinical decision-making. You will help answer: how can we provide uncertainty-aware, explainable advice for safe ECMO weaning?
Your role: You will design and develop hybrid models that combine fast physiological signals with slower patient-specific dynamics, and build an SA/UQ toolbox for high-dimensional hybrid systems. You will work with unique datasets generated from an advanced ECMO mock loop, perform simulation and data assimilation studies, and evaluate model credibility, robustness, and generalizability. Your work will be closely connected to clinical practice and includes collaboration with clinicians and interdisciplinary experts. You will publish your results, contribute to open datasets and tools, and help supervise BSc/MSc students.
Your impact: This project contributes directly to safer, more personalized care for critically ill patients. By enabling uncertainty-aware and explainable decision support for ECMO weaning, your work will reduce trial-and-error decisions and improve patient outcomes. More broadly, you will help establish trustworthy hybrid AI methodologies for safety-critical applications in healthcare and beyond.
Your environment: You will work in an interdisciplinary and supportive team at the interface of biomedical engineering, applied mathematics, and AI. TU/e offers a collaborative and inclusive research culture, with strong links to clinical partners and leading expertise in computational modelling, digital twins, and uncertainty quantification.
Deadline : 31-08-2026
(13) PhD Degree – Fully Funded
PhD position summary/title: PhD in Device Integration and Sensing for Liquid Crystal Polymer Haptic Interfaces
We are looking for a motivated PhD candidate to integrate responsive liquid crystal polymer coatings into functional haptic devices. This position is part of the Vici project “Beyond sight and sound: liquid crystal polymer-based tactile interfaces for the digital age.” The project aims to develop interactive surfaces that communicate through touch, enabling future applications in tactile displays, wearable navigation systems, prosthetics, remote communication, and immersive virtual or augmented reality.
In this PhD project, you will focus on device integration, localized actuation, and sensing. You will work with liquid crystal polymer haptic coatings that can change surface topography, stiffness, adhesion, or tactile output when triggered by electrical or optical signals. Your role will be to integrate these coatings with driving electronics and sensing schemes to create functional demonstrators, including a tactile display and wearable haptic devices.
A key scientific and engineering challenge is to create localized, addressable haptic feedback at a resolution relevant to human touch. You will investigate driving schemes based on patterned electrodes, Joule heating, capacitive coupling, thin-film transistor backplanes, and micro-LED actuation. You will also explore how the dielectric anisotropy of liquid crystal polymers can be used for sensing touch, deformation, or force.
Deadline : 01-08-2026
(14) PhD Degree – Fully Funded
PhD position summary/title: PhD in Device Integration of One-Way Transparent Optical Films for AR and Smart Visor Demonstrators
We are looking for an enthusiastic PhD candidate to integrate one-way transparent liquid crystal polymer films into functional optical demonstrators. The position is embedded in the research group of Dr. Danqing Liu at Eindhoven University of Technology and is part of the project TactVision: The Liquid Crystal Polymer Invisibility Cloak.
The project develops advanced liquid crystal polymer coatings that allow projected information to be visible from one side while remaining hidden from the opposite side. These films can enable new optical interfaces for augmented reality glasses, smart visors, privacy-protective displays, and adaptive photonic devices. This PhD project will focus on the transition from material films to working devices.
You will develop methods to integrate one-way transparent films onto curved, transparent substrates and AR-style optical components. This includes lamination, encapsulation, optical alignment, mechanical packaging, durability testing, and prototype construction. The final goal is to build functional demonstrators in which projected images are visible to the user while outward light leakage is minimized.
Deadline : 30-07-2026
(15) PhD Degree – Fully Funded
PhD position summary/title: PhD in Efficient Memory System for Infinite-Dimensional Compute
Modern high-performance applications — ranging from scientific simulations to emerging machine learning workloads — are increasingly limited by the performance and energy constraints of existing memory systems. As data becomes more complex and high-dimensional, efficiently storing, accessing, and processing this information becomes a critical bottleneck. Overcoming the memory wall requires fundamentally new approaches to memory architectures, addressing schemes, and their interaction with applications.
This PhD project focuses on designing next-generation memory systems for efficient processing of complex, multidimensional data representations. You will develop simulation frameworks to model application behavior on existing memory technologies such as DRAM and 3D-stacked memory, enabling detailed analysis of access patterns, bandwidth requirements, and latency constraints. These insights will guide the identification of key performance bottlenecks and opportunities for optimization.
Building on this understanding, you will design novel memory architectures and optimization techniques, including efficient address-mapping strategies, hardware prefetching, adaptive precision encoding, and memory-aware scheduling. You will also explore in-memory and near-memory computing approaches to reduce costly data movement and improve overall system efficiency. These innovations aim to enable scalable and predictable performance for data-intensive and irregular workloads.
Finally, you will investigate emerging memory technologies such as optical-electrical memory (OEM), which offer significant opportunities for improved energy efficiency and performance. Working in close collaboration with experts across computer architecture, software systems, and device technology, you will contribute to a holistic, memory-aware computing framework that bridges the gap between application requirements and next-generation memory system capabilities.
Deadline : 16-07-2026
(16) PhD Degree – Fully Funded
PhD position summary/title: PhD in Electronic-Structure Theory
The Computational Quantum and Molecular Dynamics group in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e) invites applications for a PhD position in electronic-structure theory. The project focuses on the development of new theoretical frameworks for describing electronic dynamics in atoms, molecules, and materials, with particular emphasis on time-dependent density-functional theory (TDDFT).
Understanding how electrons evolve in time is central to modern science and technology, from photochemistry and catalysis to quantum materials and nanoscale electronic devices. While TDDFT has become the main computational tool for simulating electronic dynamics, its predictive power remains limited in regimes where electron correlation exhibits strong spatial and temporal nonlocality.
This PhD project aims to address these limitations by developing a novel theoretical framework based on the exact electron factorization. In this approach, the many-electron wavefunction is decomposed into marginal and conditional components, leading to effective equations in which both scalar and vector potentials emerge naturally from the theory. This framework offers a new perspective on time-dependent density-functional theory and its current-density extension, with the potential to enable improved functionals capable of capturing nonlocal correlation effects.
Deadline : 31-07-2026
(17) 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 : 19-07-2026
(18) PhD Degree – Fully Funded
PhD position summary/title: PhD in Explainability and Humans in the Loop in Optimization Processes
This PhD project is a part of the CoRDS project – Confident Data-Driven Decision Support (https://www.cords-dn.at). CoRDS is a large-scale Doctoral Network, funded by the European Union under the Marie Skłodowska-Curie Actions (MSCA) Doctoral Networks programme Grant Agreement No. 101227512. It brings together 8 European universities and 14 societal partners in an ambitious interdisciplinary collaboration. Our shared mission is to advance the research on data-driven optimization methods and train a new generation of experts skilled in the combination of Operation Research and trustworthy Machine Learning. CoRDS goes beyond the state-of-the-art DDO methods by proposing decision support frameworks that combine OR and ML methods and enable robust, transparent, and fair solutions that reflect user preferences and address complex, uncertain real-world scenarios.
This PhD position belongs to the work package WP6: Transparency. As part of this doctoral project, you will develop novel knowledge representation techniques, algorithms for human-in-the-loop optimization, and interactive tools that combine graphical and natural language interfaces to gather and incorporate expert knowledge. A central research question is how human mental models of an optimization problem align or conflict with what optimization algorithms need, and how explanations can mediate that gap across the full optimization cycle.
Deadline : 30-07-2026
(19) PhD Degree – Fully Funded
PhD position summary/title: PhD in Information Systems with a focus on Trust in Digital Product Passport Ecosystems
Sustainability is one of the defining challenges of our time, placing global supply chains under mounting pressure to become more transparent, accountable, and circular. In response, the European Union is introducing Digital Product Passports (DPPs) — an emerging regulatory digital tool that accompanies products throughout their entire lifecycle to advance circular economy policy, supply-chain transparency, regulatory compliance, market surveillance, and single-market harmonization. Yet DPPs are more than digital records: they form ecosystems connecting a diverse set of stakeholders involved in the production, distribution, consumption, and end-of-life stages of products, materials, and related services through data sharing and use practices. These new data-sharing relationships create complex trust challenges, as stakeholders must develop confidence in the actors, institutions, technologies, and practices that enable collaboration among DPP ecosystem stakeholders. Successfully addressing these challenges requires approaches that go beyond regulatory compliance to enable the establishment, maintenance, and operationalization of trust among DPP ecosystem stakeholders.
Responding to these trust challenges — understanding how trust can be established, maintained, and leveraged to enable collaboration across DPP ecosystem stakeholders — is the core mission of TruPASS, a newly funded NWO research project committed to advancing the green economy transition. These two fully funded 4-year PhD positions are part of TruPASS.
As a PhD researcher, you will conduct research on trust in DPP ecosystems as a member of a multidisciplinary team spanning multiple universities. You will contribute to developing a socio-technical approach that integrates social science and technical perspectives on trust. Through systematic analysis of stakeholder needs, intentions, and operational realities, you will develop artefacts that enable supply chain actors to systematically identify, analyse, and align their trust requirements, and translate them into trust solution maps that provide input for implementation using digital identity technologies by development partners. Your work will serve as a critical conceptual bridge between social science perspectives on trust and the technical prototyping and implementation activities carried out by TruPASS development partners. You will employ Design Science Research alongside qualitative and quantitative methods, producing theoretically grounded, empirically validated and evaluated artefacts that deliver tangible trust impact in collaboration with industry partners, digital solution providers, and multidisciplinary research teams. As an Information Systems research position, this PhD offers flexibility to position your research along the socio-technical continuum — from more socially oriented IS perspectives on trust dynamics to more technically oriented IS approaches to digital identity and design.
Deadline : 09-07-2026
(20) PhD Degree – Fully Funded
PhD position summary/title: PhD in Liquid Crystal Polymer Surfaces for Mechanical Haptics
We are looking for a motivated PhD candidate to develop a new generation of responsive polymer surfaces that can communicate through touch. The position is part of a Vici project entitled “Beyond sight and sound: liquid crystal polymer-based tactile interfaces for the digital age.” The project aims to create interactive coatings that generate realistic tactile sensations for applications in human-machine interaction, assistive technology, virtual and augmented reality, prosthetics, and wearable devices.
In this PhD project, you will focus on mechanical haptics using liquid crystal polymer and liquid crystal elastomer coatings. These materials can reversibly change their shape, surface texture, stiffness, and adhesion in response to external triggers such as heat, light, or electric fields. You will design, fabricate, and characterize dynamic surfaces that form tactile features such as corrugations, Braille-like patterns, responsive fibres, suction-inspired structures, and switchable soft-hard interfaces.
The scientific challenge is to translate molecular-level changes in liquid crystal order into macroscopic surface responses that can be perceived by human touch. You will work at the interface of materials science, polymer engineering, soft matter physics, and device-oriented prototyping.
Deadline : 01-08-2026
(21) PhD Degree – Fully Funded
PhD position summary/title: PhD in Network Architectures and Control for Tropospheric and Cell-Free Networks (ANCHOR)
Our society is on the brink of a new age with the development of new visionary concepts such as internet of things, smart cities, autonomous driving, smart mobility, and coverage everywhere. This stimulates the use of new deployment concepts, such as hybrid networks and airborne network components, to support the wireless communication evolution. The ANCHOR project focuses on meeting demands on bandwidth, reliability, energy efficiency, and sustainability, as well as extended coverage from underwater, terrestrial, and aerial network components, and leverages on untapped potential of THz and optical wavelengths alongside existing radio technologies.
This PhD position focuses on Dynamic Network Architectures and Control for Tropospheric and Cell-Free Networks. You will define scenarios and requirements for tropospheric links and networks, will analyze the impact of tropospheric networking on network protocols and control schemes, and will develop network optimization and resource allocation schemes and algorithms for link and network optimization with hybrid fibre-FSO-RF communications. You will further augment software defined networking controllers to integrate tropospheric networking with hybrid fibre-FSO links and will integrate the optimization and resource allocation algorithms, followed by evaluation of network scenarios, QoS, and performance. Expected results include proposed network architectures for tropospheric networks, incl. expected KPIs and performance evaluations, and network or SDN controller plugins for tropospheric link configuration, and link and network resource optimization.
Through ANCHOR you will be provided with a comprehensive training programme covering theoretical and practical skills relevant for innovation and long-term employability in a rapidly growing sector. This highly innovative training involves experts from twenty-four international partners from academia, research institutions and industry. You will be enrolled in the PhD program of TU/e and will have the chance to benefit from secondments at some of the partner institutions as well as wide-ranging networking opportunities across the consortium.
Deadline : 22-07-2026
(22) PhD Degree – Fully Funded
PhD position summary/title: PhD in non-volatile dual-mode optical memory for unconventional computing
The exponential growth of computing capabilities has transformed nearly every aspect of modern society. Continuous advances in classical computer hardware architecture and semiconductor technology have enabled rapid progress in many fields of engineering and science. In many modern applications, particularly in advanced 3D imaging assisted characterization, sensing, simulation, vast amounts of data are generated and must be processed at extremely high speed in real-time. However, the rate of improvement in conventional computing hardware has begun to stagnate, creating a critical bottleneck with the slow-down in Moore’s scaling, limiting further performance gains. The bottleneck is in both operation speed but also the energy efficiency. Therefore, more efficient and faster compute hardware is needed. This PhD position contributes to such new hardware research.
Photonic computing hardware offers a promising alternative, leveraging the unique advantages of light for computation. Photonic processing could lead to low latency, low power dissipation, and fully utilize the intrinsic parallelism of light through wavelength or polarization multiplexing, enabling energy-efficient, high-speed, and massively parallel operations beyond the constraints of conventional electronics.
This PhD position aims to research new materials and tailor existing material properties in order to be used in a photonic memory concept for photonic computing. It should combine multi-level non-volatile storage with methodologies for both electronic and photonic read/write, resulting in the development of a novel 3D-stacked multilevel memory device, compatible with electronic and photonic chips. For that, you will use integrated photonics technology which is already shaping the future of our digital society. Ultrahigh bandwidth telecommunication, quantum and optical computing, AI datacenters, non-intrusive sensing, LiDAR, and intelligent manufacturing are all fast-evolving application fields utilizing integrated photonics, presenting opportunities for innovation and research.
Deadline : 27-07-2026
(23) PhD Degree – Fully Funded
PhD position summary/title: PhD in One-Way Transparent Liquid Crystal Polymer Films for Optical Interfaces
We are looking for a highly motivated PhD candidate to develop next-generation one-way transparent optical films based on liquid crystal polymer technology. The position is embedded in the research group of Dr. Danqing Liu at Eindhoven University of Technology and is part of the project TactVision: The Liquid Crystal Polymer Invisibility Cloak.
Modern optical systems, including augmented reality glasses, visors, head-mounted displays, and smart optical interfaces, require materials that can guide light selectively: information should be visible to the user, while remaining hidden from outside observers. This project addresses this challenge by developing cholesteric liquid crystal polymer coatings that selectively reflect projected light toward the user while maintaining high transparency from the opposite side.
The PhD project will focus on the molecular design, formulation, fabrication, and optical optimization of one-way transparent films. You will work with reactive liquid crystal mesogens, chiral dopants, photo-alignment methods, and multilayer polymer architectures. The goal is to create thin, transparent, robust films that can selectively reflect red, green, and blue light in controlled directions, enabling privacy-preserving and stealth-compatible optical displays.
Deadline : 30-07-2026
(24) PhD Degree – Fully Funded
PhD position summary/title: PhD in Optical Characterization and Photonic Performance Analysis of Liquid Crystal Polymer Coatings
We are looking for a motivated PhD candidate to study the optical and photonic performance of advanced liquid crystal polymer coatings for one-way transparency, adaptive camouflage, and smart optical interfaces. The position is embedded in the research group of Dr. Stefan Meskers at Eindhoven University of Technology and is part of the project TactVision: The Liquid Crystal Polymer Invisibility Cloak.
The project develops liquid crystal polymer coatings that can selectively control the direction, wavelength, and polarization of reflected and transmitted light. These materials are designed for optical systems in which projected information is visible to the user while remaining hidden from external observers. They also include switchable photonic coatings that can adapt their colour, reflectance, or camouflage response under external stimuli.
This PhD project will focus on the advanced optical characterization, modelling-supported interpretation, and performance benchmarking of these coatings. You will quantify how the materials reflect, transmit, polarize, scatter, and guide light under different viewing angles, wavelengths, environmental conditions, and device configurations. Your work will provide the key feedback loop between material development, optical modelling, and prototype integration.
Deadline : 30-07-2026
(25) PhD Degree – Fully Funded
PhD position summary/title: PhD in Physical control of a soft artificial heart
How can we harness embodied nonlinear mechanical and dynamical behaviour to effectively control a soft robotic heart? In this PhD, you will design effective (physical) control strategies that can harness the “embodied intelligence” present in soft robotic hearts currently under development in the Holland Hybrid Heart consortium. Current devices that we work on are driven by compressed fluid (air or liquid). Based on a simulation framework that is currently being developed, you will explore how fluidic circuits that include self-oscillatory dynamics can be harnessed to achieve a heartbeat, and how basic control strategies can be used to obtain a target physiological output in various settings.
This work builds on recent research from the group that includes the development of soft fluidic artificial ventricles/hearts (see article published in Science Advances), and fluidic circuits that self-oscillate (see articles published in Matter, Nature Communications and Science). The main goal of this PhD is to create an understanding on how such embodied behaviour can be integrated in control strategies, to e.g. reduce the need for sensory input and increase the reliability of the completely integrated system that will be eventually evaluated in animal experiments.
Your role: You will design and experimentally implement fluidic circuits that physically control the soft artificial heart, specifically circuits that create a heartbeat and that potentially implement self-regulating mechanism (such as the Frank Starling mechanism, or the baroreceptor reflex). Next, to control this dynamical system, you will determine optimal sensor placement, and implement a controller that regulates the power to the pump, to achieve target physiological performance in different circumstances. A model is currently under development to support this more experimentally targeted research. You will closely work with group and consortium members, including those that are developing soft artificial hearts and will perform first animal trials.
You will build and test physical systems by using rapid prototyping techniques, develop measurement and analysis tools, and use available simulations to guide design and interpretation. This is both a curiosity-driven project as well as an application-oriented project, at the intersection of physics, soft robotics, nonlinear dynamics, and control, with the goal of redefining how we physically control machines. Your work will help establish new design principles for soft medical devices and robotic systems in general, that can operate robustly in uncertain environments without complex control.
Deadline : 19-07-2026
(26) PhD Degree – Fully Funded
PhD position summary/title: PhD in Process-Aware AI Agents for Autonomous Manufacturing at Process Analytics Cluster
The Process Analytics (PA) group in the Department of Mathematics and Computer Science at TU/e is seeking a PhD candidate to work on the EuroFMX project (a large-scale Horizon Europe initiative bringing together 69 partners from 20 countries). EuroFMX aims to develop the world’s first industrial-native autonomiation foundation model for manufacturing, built on Physics-Informed Graph Neural Networks embedded within transformer architectures over knowledge graphs. The project pursues a paradigm shift from reactive factory automation to proactive autonomisation, where AI agents autonomously optimize production workflows, predict disruptions, and dynamically reconfigure production systems.
The PA cluster is one of the leading research groups worldwide in process mining. The cluster develops theory, algorithms, and tools that bridge the gap between process science and data science.
Deadline : 11-07-2026
(27) PhD Degree – Fully Funded
PhD position summary/title: PhD Spotsize converter development In Photonic Integration Group
PICs on InP have the big advantage to integrate all required optical functionalities in a single chip and hold great promise to address different markets powering AI, datacenters but also in sensing for automotive and medical applications. However, coupling light efficiently between optical fibers and the high-confinement waveguides on an InP chip remains a fundamental packaging challenge. Spot Size Converters (SSCs) are essential building blocks to ease the packaging and minimize interface losses. As PICs scale in complexity and packaging becomes a bottleneck, relying on purely theoretical simulations for these interfaces is insufficient; there is a critical need to bridge the gap between idealized designs and real-world fabrication tolerances.
We are looking for a PhD student to work on advanced design methodologies for spot size converters on InP PICs. The work will begin at the device level, exploring, simulating, and experimentally verifying various SSC architectures (such as lateral/vertical tapers or multi-layer transitions) to optimize mode matching, polarization dependency, bandwidth, and fabrication tolerance.
A core focus of your research will be establishing a systematic methodology to analyze experimental test results and translate that characterization data back into the design process.
Furthermore, you will collaborate in the PhI group to establish and expand a framework for compact models that builds on this experimental characterization data, ensuring that circuit designers have access to highly accurate, data-driven building blocks within the PDK.
Deadline : 25-07-2026
(28) PhD Degree – Fully Funded
PhD position summary/title: PhD Transient Fischer–Tropsch Catalysis
Are you fascinated by the challenges of the energy transition and catalytic conversion of carbon-based feedstocks? We are looking for a motivated PhD candidate to investigate how feedstock composition and catalyst structure affect performance during transient Fischer–Tropsch synthesis (FTS).
In this project, you will focus on understanding how variations in CO/CO₂/H₂ ratios and the presence of contaminants influence catalytic activity, selectivity (e‑naphtha to e‑wax), and long-term stability. By combining advanced kinetic studies with state-of-the-art operando characterization techniques, you will develop fundamental insights that enable improved catalyst and process design.
This project is embedded in the HyCarb consortium, where academic and industrial partners collaborate on advancing sustainable carbon conversion technologies under realistic operating conditions.
Deadline : 15-08-2026
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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