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: PhD position in qubit hardware development and implementation for hybrid quantum computing
At TU/e, the ultracold atom laboratory of CQT, part of the Center for Quantum Materials and Technology (QT/e), is developing neutral‑atom quantum computing platforms based on Rydberg interactions. This project is part of the KAT‑1 Quantum Delta NL program on hybrid quantum computing, a demonstrator built on Quantum Inspire, the European quantum computer that offers 24/7 online access.
The qubit hardware that we operate in our laboratories are two neutral‑atom quantum computing platforms (rubidium and strontium). Optical tweezer arrays provide programmable and precise control and site‑resolved single‑atom readout. Rydberg excitation enables strong nearest‑neighbor coupling for multi‑qubit entanglement. The architecture is highly scalable to large qubit counts via array reconfiguration, atom shuttling, and parallel control—offering a promising route to fault‑tolerant quantum computing. The platforms are built for stable, continuous operation with 24/7 online access for community use. More information: www.tue.nl/rydbergQC.
Concretely, the candidate will focus on:
- Development of the qubit hardware for fast and robust operation of our strontium-based quantum processor unit
- Employment of pulse-optimized multi-qubit gates that reduce the overall gate time (using intensity- and frequency-shaped laser pulses)
- Development of a complete and scalable quantum instruction set optimized for large qubit arrays and application of initial quantum simulation use cases
- Implementation of quantum error correction protocols optimized for Rydberg quantum computers to achieve the best performance
Deadline : 02-03-2026
(02) PhD Degree – Fully Funded
PhD position summary/title: PhD position in algorithm and user-interface development for hybrid quantum computing
Are you passionate about quantum technologies and eager to further develop a Rydberg atom quantum computing platform as a 24/7 user-facility for hybrid quantum comping? Join us! We are looking for a candidate that drives the theory of Rydberg atoms in optical tweezers, algorithm and software development for our full-stack quantum computers.
Deadline : 02-03-2026
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(03) PhD Degree – Fully Funded
PhD position summary/title: PhD on Tomographic Volumetric Additive Manufacturing: a unified and validated multiphysics process model
Are you fascinated by cutting-edge additive manufacturing technologies, eager to develop a validated and predictive multi-domain computational model of this novel manufacturing process and motivated to cooperate with another PhD student who will develop the experimental setup and material models? Join us as a PhD candidate in a 2 PhD program and contribute to making volumetric 3D printing predictable, reliable, and industry-ready.
Deadline : 11-03-2026
(04) PhD Degree – Fully Funded
PhD position summary/title: PhD on contract-based design of complex dynamical systems
Are you passionate about dynamical systems and system theory? Are you interested in making the design process of complex dynamical systems simpler by smart new tools for systems engineering? Are you eager to apply and valorize scientific results in this field in high-tech domains such as semiconductor machines and robots, together with highly innovative companies? Would you like to work in a team of 4 PhD students? Then, these PhD positions are made for you!
Deadline : 20-03-2026
(05) PhD Degree – Fully Funded
PhD position summary/title: PhD on Compact models of optical amplifiers for Photonic Integrated Circuits
Photonic integrated circuits rely heavily on the accuracy of the tools used to design them. As the foundry model matures, Process Design Kits (PDKs) have become the standard interface allowing fabless designers to create complex chips. Next to passive components, our InP platform provides the unique capability to integrate these with active components such as Semiconductor Optical Amplifiers (SOAs). To fully exploit the potential of InP technology, reliable physics-based models are required that allow designers to accurately predict circuit performance before fabrication. SOAs present complex, non-linear behaviours that pose challenges to model in a simplified and efficient way. They require multi-dimensional parametric models to describe noise, nonlinearity, modes and spurious reflections, but an efficient design flow will require a simple implementation.
We are seeking a PhD researcher to develop robust and efficient compact models for SOAs within the InP platform. The work will bridge the gap between device physics, process optimisations and practical circuit design. You will be responsible for the full modelling cycle: from designing specific test structures to isolate physical phenomena, to performing detailed electro-optical characterization in the lab. Using this data, you will define parametric models that capture among others gain, saturation, and noise dynamics.
The goal is to deliver a verified model that a designer can confidently use within a broad range of design workflows for designing complex PICs.
Deadline : 06-03-2026
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(06) PhD Degree – Fully Funded
PhD position summary/title: PhD in Transparent AI Decision Support Systems for Sustainable Mobility
This PhD project is a part of The DECIDE project – Democratizing AI: Empowering Citizens through Transparent Decision-making. DECIDE is a large-scale, NWO-funded research initiative under the Dutch Research Agenda (NWA). It brings together 10 Dutch universities, over 50 academic researchers, and 30 societal partners in an ambitious inter- and transdisciplinary collaboration. Our shared mission is to design and implement a new generation of transparent, citizen-empowering AI systems, with a focus on AI-supported decision-making that contributes to the empowerment and democratic participation of citizens.
The DECIDE project addresses real-world challenges across domains such as healthcare, mobility, public governance, and healthy lifestyles. By co-developing strategies with societal stakeholders, DECIDE aims to create solutions that have tangible impact on society.
Deadline : 26-02-2026
(07) PhD Degree – Fully Funded
PhD position summary/title: PhD 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.
Deadline : 30-04-2026
(08) PhD Degree – Fully Funded
PhD position summary/title: PhD in Super-Resolution microscopy of clinical biopsies for precision medicine
Super-resolution optical imaging has transformed cellular and molecular biology allowing to visualize biological process at the molecular level. However, translating these powerful techniques to clinically relevant samples and precision medicine remains a major scientific and technological challenge. This PhD project aims to bridge this gap by developing and applying super-resolution microscopy to study cancer biomarkers at the nanometer and single-molecule level in real patient material.
In this project, you will:
- work with cell line models and lung cancer biopsy samples in close collaboration with clinicians at the Catharina Hospital in Eindhoven
- set up and optimize advanced imaging strategies to visualize and quantify cancer-related biomarkers with unprecedented spatial resolution.
- – combine cutting-edge microscopy with quantitative data analysis, you will investigate how molecular organization and heterogeneity at the nanoscale relate to targeted drug mechanisms of action and individual patient responses.
Precision medicine-matching each patient with the most effective treatment-remains one of the grand challenges in oncology. Despite the availability of targeted therapies, patient responses vary widely, and the molecular origins of treatment resistance are often poorly understood. This project addresses this challenge by introducing novel imaging approaches capable of revealing subtle, nanoscale differences in biomarker expression and organization that are invisible to conventional techniques. Your research will contribute to a deeper understanding of therapy response and resistance and may lay the groundwork for new diagnostic and stratification tools with direct impact on healthcare.
Deadline : 28-02-2026
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(09) PhD Degree – Fully Funded
PhD position summary/title: PhD in Smart Reactive Power Control for Future Power Grids
Are you our next PhD researcher in exploring cutting-edge probabilistic forecasting, optimization, and real-time control for smart reactive power management in future power grids?
We are seeking a highly motivated and talented PhD candidate to join the SPARC – Smart Provision of Ancillary Reactive Power Control project, a collaborative research initiative between TU/e (Eindhoven, Netherlands) and TUM (Munich, Germany). This project aims to revolutionize reactive power management in modern power systems by developing advanced probabilistic forecasting models, integrated optimization frameworks, and real-time control architectures. The successful candidate will work at the intersection of electrical power system dynamics, control, optimization, and machine learning, with a focus on enhancing grid resilience and enabling the integration of renewable energy.
The SPARC project addresses the pressing challenge of managing reactive power flows at the transmission-distribution (T-D) interface in power systems with high penetration of renewable energy and power electronics. The PhD candidate will contribute to:
- Developing probabilistic forecasting models for reactive power availability, leveraging machine learning and real-time data.
- Designing an integrated optimization framework for ancillary service coordination, considering grid constraints and market dynamics.
- Creating real-time control architectures for seamless TSO-DSO interaction, validated through Power Hardware-in-the-Loop (PHIL) simulations.
- Collaborating with grid operators like Enexis to ensure practical relevance and impact.
Deadline : 02-03-2026
(10) PhD Degree – Fully Funded
PhD position summary/title: PhD in Photonic and Electronic Integration of Detectors for Wireless Optical Communication
This project aims to innovate detector design to overcome these limitations and improve performance in indoor optical wireless systems. The proposed project will focus on creating components, in particular integrated photonics-electronics components that can support optical wireless communication for mass-market client devices and pave the way for miniaturization and cost reduction. In the initial stage, the project will model the entire communication chain to understand and optimize the detector and its parameters. In this project, you will focus on developing a suitable solution for integrating photo detectors with the analog front-end. You will work on this solution, ensuring it is scalable, has a wide aperture (to maintain a good signal-to-noise ratio), a wide bandwidth, and avoids mechanically movable components. Nonetheless, it must be able to capture light signals from a range of directions, including distributed MIMO and angular diversity. In this work, you will examine several trade-offs involved in scaling detector arrays, including bandwidth limitations, interconnect considerations, and electronic design (buffers, TIA, etc.). Your work will focus on implementing TIA and buffer circuits in existing advanced semiconductor technologies, such as silicon or III-V. A key challenge in this activity will be to consider the heterogeneous integration (HI) methodology. The consideration of HI for the PD-TIA array system involves integrating a PD array, typically implemented in a III-V technology, such as InP, with a Buffer-TIA array implemented in a high-speed Silicon technology, such as SiGe BiCMOS. In the project, you will have the opportunity to design, fabricate, and measure electronic ICs in either silicon or III-V semiconductor technologies, which will form part of a demonstrator for a PD-Buffer-TIA configuration, thereby validating the requirements of a detector for optical wireless communication applications.
Deadline : 04-03-2026
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(11) PhD Degree – Fully Funded
PhD position summary/title: PhD in Resilient Wireless Communications for Mission Critical Applications
At the Advanced Networking Lab of the Center for Wireless Technology Eindhoven (CWTe) of the EE Department of TU/e we have several open Ph.D. positions on ultra-reliable wireless communications for mission critical applications in aerospace.
The Advanced Networking Lab is a member of CWTe which is part of the TU/e, Department of Electrical Engineering. Researchers from the groups Electromagnetic, Integrated Circuits, Signal Processing Systems, Optical Communication, and Electronic Systems, work together to address research questions across these research areas from the wireless channel through various layers of the communication stack (www.tue.nl/cwte).
The ANL is currently involved in many beyond-5G/6G R&D projects funded by the European commission and Dutch government. Our key research areas include ultra-reliable low latency communications, resource allocation, digital twins and Open RAN for 6G networks, distributed massive MIMO, flexible compute continuum for 6G RAN open architectures. A substantial part of our research is dedicated to explore the feasibility of wireless communications to replace the existing wired avionic networks onboard aircraft. Research activities in this domain are supported through the active R&D projects RHIADA and Luchtvaart in transitie (LiT) or Aviation in Transition (in English). The laboratory facilities include fully opensource OpenRAN-compliant 5G testbed with commercial radio units (RUs) and software-defined radios, a fully shielded metal room in which the most sensitive electronics can be measured, a 28 m2 anechoic chamber (500MHz-40GHz), system integration lab where chips can be linked to other components, and a 16-channel distributed MIMO testbed (sub-6 GHz).
Deadline : 15-03-2026
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(12) PhD Degree – Fully Funded
PhD position summary/title: PhD in Scalable Safe AI for Semiconductor Metrology
Industry context and motivation. Pretrained foundation models bring lots of potential in semiconductors metrology. Multimodal foundation models combining data from multiple metrology sources, enable high accuracy in the early stages of next generation manufacturing process, and across many different usecases. As the process matures and enters high volume manufacturing, these models can be specialized through efficient fine-tuning and distillation to improve throughput in terms of both compute and data acquisition, while maintaining guaranteed performance on critical failure modes.
This PhD project focuses on developing data-efficient methods for fine-tuning and distillation under strict (customer fab) data and privacy constraints while providing performance guarantees for specific downsteam tasks.
Customer data can be indeed highly constrained. In particular, customers might not allow data to leave the fab, and datasets that are available can be limited and imbalanced.
The model performance requirements are expected to evolve over time – from early research to high-volume manufacturing. ML models should be able to adapt to changing accuracy, speed, and defect-detection requirements across the full lifecycle of a process node, and should cover a wide variety of use cases, across different customers, with minimal adaptation.
Developing beyond state-of-the-art techniques enabling such ML behaviour of ML would have a profound impact – facilitating more robust metrology models and significantly faster time-to-recipe across the maturity stages of a semiconductor process node.
Deadline :18-03-2026
(13) PhD Degree – Fully Funded
PhD position summary/title: PhD in Personalized hemodynamic modelling of carotid artery stenosis for stroke prediction
At the faculty of Biomedical Engineering at Eindhoven University of Technology, a PhD-position is available within the Thromborisk project, a Marie Sklodowska-Curie Actions (MSCA) Doctoral Network funded by the EU. The position is open for appointment during the course of 2026-2030, with a duration of 48 months hosted by TU/e, and includes secondments at the partner institutes within the consortium for a cumulative period of 12 months.
As a doctoral candidate (DC), you will be embedded in the Cardiovascular Biomechanics group, led by Prof. Huberts, which focuses on computational and experimental biomechanical analysis of the cardiovascular system and its application to clinical diagnosis, prognosis, and intervention. Within this collaborative environment, you will work closely with Maastricht University Medical Center, participate in secondments, and supervise BSc/MSc students on related projects.
Your main challenge will be to develop personalized in silico and in vitro hemodynamic models of the carotid artery bifurcation. These models will form the basis of a tool to evaluate the potential for carotid artery thrombus embolization, ultimately contributing to clinical trials aimed at predicting patient-specific stroke risk.
This position offers the opportunity to contribute to research with high societal impact: improving stroke risk prediction and supporting personalized treatment strategies.
Deadline : 31-03-2026
(14) PhD Degree – Fully Funded
PhD position summary/title: PhD in Patient-specific estimation of local variation of intraluminal thrombus properties in AAAs
ThromboRisk – European Doctoral Network From cells to systems: Pioneering multi-level thrombosis risk prediction models consortium is funded by the European Union under Grant Agreement No. 101227706. It groups 10 hiring universities: Eindhoven University of Technology (NL), University of Maastricht (NL), University of Amsterdam (NL), Catholic University Leuven (BE), Charité – Universitätsmedizin Berlin (DE), University College London (UK), Transsilvania University of Brasov (RO), SANO Centre for Computational Personalized Medicine (PL), University of Leeds (UK), and University of Bern (CH).
ThromboRisk will develop an integrated platform to advance our understanding of thrombosis across biological scales, combining mechanobiology, biochemistry, pathophysiology, and computational modelling. For this inclusion to occur, each DC will develop through their research a unique contribution to the multi-level thrombosis risk prediction framework, addressing specific aspects of thrombus formation, growth, rupture, and clinical impact. This hands-on training is supplemented with several scientific professional courses and an immersive training program where the DCs can fine-tune their skills for the jobs of tomorrow, while addressing the societal challenges of the ThromboRisk program.
Deadline : 31-03-2026
(15) PhD Degree – Fully Funded
PhD position summary/title: PhD in Optimal Operation of Multicommodity Energy Systems from a Market and Grid Perspective
The BACH (Brainport Approach for a Congestion-free Holland) project addresses the increasing congestion in Dutch electricity distribution grids caused by the rapid growth of renewable energy generation and increased electrification of energy demand. Due to limited grid capacity and long connection queues, customers can no longer get a new connection, or expansion of an existing connection, this holds for both demand and supply. A large share of renewable generation cannot be connected or must be curtailed.
BACH develops a data-driven and proactive approach to congestion mitigation by integrating electricity, heat and gas systems into a multicommodity energy system. The optimal operation of a multicommodity energy system is demonstrated on TU/e campus. The real-life project data and results will be enriched to make them applicable on a large scale. Using distribution grid data, customer profiles and energy transition scenario’s, the project identifies where storage and conversion technologies can be deployed most effectively in the future to relieve grid congestion and increase renewable energy integration.
A central element of BACH is the development of an open-architecture Multicommodity Energy Management System (MC-EMS) and associated impact analysis tools, which together enable grid-aware and market-aware operation of multicommodity energy hubs. The concepts and tools are validated in a real-life mirror location at the TU/e campus and designed for scalability towards regional and national application.
This PhD project focuses on the modeling and optimal operation of multicommodity energy systems under explicit distribution grid constraints. The research addresses how real grid congestion information can be systematically incorporated into system models and operational decision-making.
Deadline : 14-03-2026
(16) 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.
Deadline : 15-03-2026
(17) PhD Degree – Fully Funded
PhD position summary/title: PhD in Multiscale modelling of recycled stainless green steels
The transition to low-carbon steelmaking will significantly alter production chains, starting from raw materials to high-end products made from these steels. One of the paths to reduce CO2 emissions in steelmaking is the use of scrap. This will inevitably entail significant variations in the chemical composition of the steel produced, which may affect the performance of the steels during processing and service. This is particularly true for stainless steels undergoing phase transformations in view of the targeted mechanical properties.
These phase transformations occur during the metal forming processes in which they are shaped to a product. These need to be well controlled to safeguard the formability of the material during processing on the one hand and securing the resulting strength and hardness properties on the other hand. This makes this class of materials particularly vulnerable to the use of recycled material, with possibly uncontrolled or variable content. Accordingly, this research project aims to establish predictive insights between microstructures contaminated with tramp elements and the resulting engineering properties as required for product processing.
Vacancy for a modelling-oriented PhD student on the Constitutive behaviour of recycled metastable stainless steel
This project will focus on a model-based approach, taking experimental input from the partners involved, to assess the influence of scrap-induced changes and contaminations on the performance of stainless steels that are subjected to phase transformations in the production chain. This approach will enable a qualitative and semi-quantitative assessment of the influence of the presence of various elements (and second-phase particles) on stainless steel properties. This step is essential for the introduction of green steels in the market segment exploiting such steels.
Deadline : 29-03-2026
(18) PhD Degree – Fully Funded
PhD position summary/title: PhD in Hybrid FSO-RF-Fiber Channel Models and Transmission Schemes (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 optical wavelengths and THz frequencies, alongside existing radio technologies.
This PhD position focuses on Hybrid FSO-RF-Fiber Channel Models and Transmission Schemes, developing hybrid FSO-RF-Fiber links for flexible front-/mid-/backhauling in terrestrial and tropospheric networking scenarios. You will analyze the combined FSO-RF-Fiber channel and develop accurate but sufficiently lightweight channel models and come up with jointly optimized schemes for such hybrid links and networks, incl. waveforms, modulation, coding and/or low-level protocols. You will further design and propose link architectures and conduct relevant experimental evaluations. The expected outcomes include channel models and architectures for hybrid FSO-RF-Fiber links, incl. theoretical, simulation and experimental analysis, supported by relevant simulation software/tools for hybrid links. Joint modulation formats, coding and low-level protocols jointly optimized for hybrid channels are further expected to be created.
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 : 02-03-2026
(19) PhD Degree – Fully Funded
PhD position summary/title: PhD in Event-Based Sensor Fusion Algorithms for Real-Time Perception and Control
Autonomous systems, such as collaborative robots or drones, must perceive and react to their environment in milliseconds. Traditional perception pipelines process data in “frames” (snapshots in time), which introduces unavoidable latency and high data redundancy. Furthermore, fusing distinct modalities, like the spatial depth, speed and direction of moving objects from FMCW Radar and the high temporal resolution of Dynamic Vision Sensors (DVS/Event Cameras), remains a complex computational challenge.
As a PhD candidate at the Neuromorphic Edge Computing Systems (NECS) Lab, you will develop “event-based” algorithms that fuse these two sensory worlds. You will move beyond simple classification and target real-time perception and control tasks.
Deadline : 02-03-2026
(20) PhD Degree – Fully Funded
PhD position summary/title: PhD in End-to-End AI for Mobile Autonomous Robotics
This PhD position concentrates on designing innovative AI architectures and training techniques for end-to-end robotics. Specifically, you will investigate Vision Language Models (VLMs), Multi-modal Large Language Models (MLLMs), and Vision Language Action models (VLAs) for applications involving spatial scene understanding and spatial reasoning. The challenge is achieving a level of spatial understanding that allows robots to handle new environments and tasks with minimal human-provided demonstrations or descriptions, while maintaining a certain level of reliable and safe operation. Another challenge particular to robotics is that these end-to-end models must be efficient enough to operate in real time. Certain tasks, e.g., collision prevention in manipulation, require guaranteed timely responses, while others, e.g., reasoning, are allowed to take longer. These ‘fast’ and ‘slow’ tasks need to be supported simultaneously by the end-to-end architecture and should be compatible with the limited compute and energy resources of embedded systems.
Your daily activities will include reviewing the latest developments in the field, identifying current limitations, hypothesising possible causes and solutions, designing improved network architectures and training methods, setting up validation methods to test hypotheses, and reporting on findings via presentations and publications. It is expected that you perform these tasks as a professional with great independence, and that you possess the ability to engage in in-depth discussions, make informed choices, and be open about the limitations of your research. Additionally, given the rapid pace of AI progress, we find it very important that PhDs not only work individually but also be eager to work in teams with students, peers, and supervisors.
Deadline : 04-03-2026
(21) 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.
Deadline : 21-03-2026
(22) PhD Degree – Fully Funded
PhD position summary/title: PhD Coordinator (0,8 – 1 FTE)
We are looking for a warm, proactive, and approachable PhD Coordinator to support and strengthen the PhD community within the Department of Chemical Engineering & Chemistry (CE&C). In this role, you serve as a low thresh hold and trusted point of contact for PhD candidates. From the moment a new PhD joins the department, you help guide them through their onboarding, ensure that key documents such as the Training & Supervision Plan are completed on time, and make sure the administrative aspects of their trajectory are well ‑organized.
As this is a newly established role within our faculty, we are looking for someone who feels confident shaping the position, building its foundations, and finding their own way in setting up effective practices. You enjoy taking initiative, creating structure where needed, and developing the role in a way that best supports our PhD community.
Throughout the entire PhD journey, you remain a consistent and reliable presence. You monitor wellbeing and identify challenges early—whether they are practical, organizational, cultural, or personal in nature. By offering a listening ear, sharing advice, or referring someone to the right support, you contribute to preventing delays or dropout and help ensure that PhD candidates feel seen and supported.
A core part of the role involves acting as a bridge between PhD candidates and the wider organization. You help clarify expectations, support smooth communication, and connect colleagues across the Graduate School, student advisers, and PhD coordinators in other faculties. You also contribute to community building by organizing workshops, peer sessions, and informal gatherings that strengthen connection and wellbeing.
Deadline : 20-03-2026
(23) PhD Degree – Fully Funded
PhD position summary/title: 18 PhD positions in the ThromboRisk MSCA Doctoral Network
Are you ready to shape the future of personalized medicine? Do you want to work at the intersection of computational modeling, vascular biology, and clinical innovation? The ThromboRisk Doctoral Network offers an exceptional opportunity to join a European-wide research initiative tackling one of the most pressing challenges in cardiovascular health: thrombosis.
Funded by the Marie Skłodowska-Curie Actions (MSCA) under Horizon Europe, ThromboRisk brings together 17 leading universities, industry, hospitals and research institutes to train 18 Doctoral Candidates (DCs) in a highly interdisciplinary and collaborative environment.
Deadline : 26-02-2026
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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