ETH Zurich, Switzerland 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 ETH Zurich, Switzerland.
Eligible candidate may Apply as soon as possible.
(01) PhD Degree – Fully Funded
PhD position summary/title: Doctoral Position on viscoelastic hydrogels for 3D mechanobiology and tissue repair
In the first part of the project, the doctoral student will synthesize PEG- and hyaluronic acid-based DCHs cross-linked via dynamic covalent chemistries (boronate ester, hydrazone, imine, and disulfide bonds), spanning a broad range of moduli (0.1–100 kPa) and relaxation times (10–1000 s) to mimic the mechanical properties of native and cancer-associated dermal and cartilaginous tissues. The student will fabricate and characterize these materials using rheology and nano-indentation, guided by molecular design insights from the broader project team. In the second part, the student will apply these tailorable DCH scaffolds to address key mechanobiology questions. These studies will provide mechanistic insight into how matrix viscoelasticity drives key tissue-level biological processes.
The doctoral student will work in close collaboration with and be supported by an interdisciplinary team of doctoral students and postdocs working on related topics, including Dr. Céline Labouesse, a Senior Scientist in the lab, and Dr. Philipp Fisch, both experts in cell mechanobiology. In addition to research, the PhD candidate is expected to contribute to lab duties and teaching, including student supervision, lecture support, and practical courses in the lab.
Deadline : Open until filled
(02) PhD Degree – Fully Funded
PhD position summary/title: Doctoral Student Range shifts of mountain plants
Many species are not shifting their ranges fast enough to successfully track climate change, yet attempts to predict the velocity of plant range shifts have generally failed. Partly this is because we still lack empirical data to quantify the processes needed for species to disperse to new locations and establish viable populations there. We have a particularly poor grasp of long-distance dispersal rates, and how propagule origin and biotic interactions constrain establishment beyond species’ ranges. Focusing on mountain plants, this project aims to disentangle the importance of the species interactions and demographic processes governing shifts of plants’ high-elevation range limits. It will further test whether an improved understanding of these processes can be used to predict range shift ability across ecosystems. By combining global, standardized experiments across six continents within the Mountain Invasion Research Network (MIREN), and detailed studies in two core mountain regions (Swiss Alps, Canadian Rocky Mountains), the project robustly tests competing hypotheses about mechanisms of range shifts while generalizing across a diverse geographic context.
Deadline : 17th August 2026
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(03) PhD Degree – Fully Funded
PhD position summary/title: Doctoral Position in Robust Railway Intervention Planning under Uncertainty
Railway infrastructure provides substantial capacity for the movement of people and goods, yet this capacity is reduced or suspended whenever there are interventions. Interventions, which range from condition monitoring through minor and major maintenance to renewal and expansion, require time, money, machines, and personnel, and they partially block access to the track. Consequently, traffic and timetables must be modified, and passengers are affected. Decisions on when, where, and how such interventions should be grouped or separated, and on how the associated timetables should be planned, must be taken well ahead of execution and under considerable uncertainty regarding maintenance costs, timetable feasibility, and passenger impact.
These decisions are challenging because the relevant effects and factors are difficult to quantify and are inherently uncertain, whilst decision-making power is distributed across several stakeholders, asset levels, and time horizons. The coordination process is at present largely qualitative and iterative, and it offers limited scope for the systematic use of predictive, quantified information. Multiple trade-offs must therefore be balanced, including direct economic costs, the availability of contractor resources, short-term effects on passengers such as longer journeys and additional transfers, and long-term effects such as the erosion of trust and of political support for railway funding.
This ETH Mobility Initiative project addresses these gaps by developing quantitative support for fact-based decision-making in railway infrastructure management. Particular emphasis is placed on characterising and propagating the uncertainties inherent in intervention planning and resource forecasting, on developing robust optimisation methods that determine when, where, and how interventions should be grouped or separated, and on integrating the resulting information within a geospatial decision-support environment aligned with ISO 55001 and UIC best practices. The advertised position contributes to this line of work, with validation carried out against historical and planned data for a pilot SBB corridor.
Deadline : Open until filled
(04) PhD Degree – Fully Funded
PhD position summary/title: PhD Researcher – Agentic AI
The increasing availability of multimodal health data, coupled with advances in AI, offers new opportunities to deliver personalised, scalable behavioural interventions in healthcare. However, current conversational AI systems remain limited in their ability to operate reliably across diverse clinical contexts. They often lack generalisability, struggle to integrate heterogeneous and longitudinal data, and do not adequately address key challenges related to safety, bias, robustness, and clinical validity.
We are seeking a highly motivated PhD candidate to join an interdisciplinary Future Health Technologies (FHT) research programme at the forefront of agentic AI in healthcare. The project aims to design, develop, and evaluate AI-driven coaching and nudging systems capable of supporting positive patient outcomes across a range of conditions.
The successful candidate will contribute to the development of a scalable and interoperable AI system that delivers personalised behavioural support across domains such as mental well-being, stroke rehabilitation, falls prevention, and chronic disease management. Central to this work is the integration of multimodal data ingestion, knowledge retrieval, and adaptive decision-making to deliver high-quality health/ behaviour change interventions with a strong emphasis on performance, safety, and real-world applicability.
By bridging machine learning, behavioural science, and clinical research, the project seeks to establish foundational methods for trustworthy agentic AI systems that can be deployed across diverse healthcare settings.
Deadline : Open until filled
(05) PhD Degree – Fully Funded
PhD position summary/title: Doctoral Position in circularly polarized attosecond pulses and chiral charge migration
The position is part of Attochirality, a new ERC Advanced Grant project that will establish attosecond chiroptical pump-probe spectroscopy. The project combines a 100 kHz few-cycle Yb-fibre laser, circularly polarized isolated attosecond pulses, tuneable 1-2 fs deep-ultraviolet and near-infrared pulses, and electron-ion coincidence detection in a reaction microscope. The scientific programme addresses chiral electronic wave packets, charge migration and charge transfer, and the transition from isolated to microhydrated and fully solvated molecules.
This doctoral project focuses on the generation and complete characterization of circularly polarized isolated attosecond pulses and on their application to chiral charge migration. The experiments will use three-dimensional electron-ion coincidence spectroscopy to observe and understand chiral charge migration.
Deadline : Open until filled
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(06) PhD Degree – Fully Funded
PhD position summary/title: PhD Researcher in Trustworthy and Human-Centred AI Development
As a PhD Researcher, you will pursue independent research on trustworthy and human-centred AI while contributing to the systems we are building in this space. The work spans methodological research and concrete implementation, and treats questions of reliability, accountability, and human-AI interaction as research questions in their own right and not as deployment afterthoughts. You will work alongside researchers and engineers in an international team and will have the room to shape the direction of your own thesis. An example of a system to be developed are trustworthy AI voting assistants in the context of Supported Democracy.
Deadline : 31 July 2026
(07) PhD Degree – Fully Funded
PhD position summary/title: PhD Position in Cryogenic Carbonate Mineralization and Carbon Dioxide Fluxes
In this role, you will investigate how freezing influences carbonate mineral formation and carbon dioxide exchange under conditions simulating cryogenic environments such as glaciers, sea ice, permafrost, and ice-rich planetary systems. You will help contribute to a new experimental platform for low-temperature geochemistry at ETH Zurich and engage in an emerging research direction focused on how ice formation influences carbon cycling in cold environments. The project combines controlled freezing experiments, aqueous geochemistry, gas monitoring, and mineral characterization to develop a process-based framework for understanding when freezing promotes carbon release, carbon storage, or both.
Deadline : 31 August 2026
(08) PhD Degree – Fully Funded
PhD position summary/title: PhD Position in NCCR Genesis: On the rise of molecular complexity at the Origin of Life
The successful candidate will investigate the emergence of molecular complexity from simple building blocks, such as amino acids and nucleotides, toward self-replicating chemical systems. The project will focus on peptide amyloids and their interactions with RNA and membranes, with the long-term goal of understanding the emergence of proto-ribosomal machinery.
Deadline : Open until filled
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(09) PhD Degree – Fully Funded
PhD position summary/title: PhD Positions in Neurotechnology: Next Generation Non-invasive Neurotechnologies & Brain Therapies
We develop advanced focused ultrasound technologies, adaptive closed-loop control algorithms, and ultrasound-responsive micro/nano therapeutic carriers as an integrated precision drug delivery platform for the brain. Our approach employs a novel two-component ultrasound strategy that first concentrates drug carriers within the target region and subsequently triggers localized drug release, achieving high therapeutic concentrations exclusively at the desired site while minimizing systemic exposure. To ensure safe, precise, and reproducible therapy, the platform continuously monitors acoustic emissions from carriers, automatically adjusts ultrasound exposure in real time, and preserves blood–brain barrier integrity. By integrating therapeutic carrier design, robotics, medical imaging, real-time sensing, and intelligent control into a unified system, this technology establishes a new paradigm for precision neurotherapeutics and opens new opportunities for treating epilepsy, brain tumors, neurodegenerative diseases, psychiatric disorders, and other neurological disorders. The long-term goal of this project is to establish this technology as a clinically applicable therapeutic platform by integrating targeted drug delivery with advanced neuroimaging, electrophysiology, and closed-loop neuromodulation. The primary objective of this PhD project is to advance this technology from proof-of-concept toward clinical translation. Successful candidates will further develop the technology, investigate its therapeutic potential for neurological disorders, perform preclinical validation in rodent and large-animal models, and contribute to the preparation of first-in-human clinical studies. The project is conducted in close collaboration with Swiss Epilepsy Clinic Lengg, the Department of Neurosurgery at University Hospital Zurich, and the Vetsuisse Faculty of the University of Zurich.
Deadline : Open until filled
(10) PhD Degree – Fully Funded
PhD position summary/title: Doktorand:in für Risikokommunikation und KI
Nach jedem grösseren Erdbeben steigt kurzfristig die Wahrscheinlichkeit, dass ein ähnlich grosses oder gar ein grösseres Beben folgt. Obwohl die Wahrscheinlichkeit deutlich ansteigt, bleiben die absoluten Werte oft klein. Dies macht eine verständliche und adressatengerechte Kommunikation herausfordernd. Im Projekt zu Erdbebenwahrscheinlichkeit gehst du der Frage nach, wie man solche Informationen für verschiedene Anwendungsbereiche verständlich, hilfreich und ansprechend aufbereitet. Zudem wirkst du im europäischen Projekt EARTH-AID mit, wo wir agentenbasierte KI entwickeln, welche die Beurteilung von geologischen Gefahren unterstützen sollen. Du untersuchst, wie unterschiedliche Nutzer:innen diese verwenden und beurteilen.
Deadline : Open until filled
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(11) PhD Degree – Fully Funded
PhD position summary/title: PhD position in Modeling and Control of Nonlinear Dynamical Systems from Data
- The selected candidate will work on the furhter development of spectral submanifold (SSM) methods, aiming to further develop concepts in nonlinear model reduction
- Possible research directions include the development of nonlinear modelling and control approaches for fluid-structure interactions, material with memory, soft robotics, tele-operated systems, lightweight space structures, and turbulent flows
- Ongoing and prior work in related areas by the Chair in Nonlinear Dynamics can be found at the group website
- The position will be based at either the Department of Mechanical and Process Engineering or the Department of Mathematics, depending on the candidate’s background and preferences
- The starting date is flexible but an early start is preferred
Deadline : Open until filled
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(12) PhD Degree – Fully Funded
PhD position summary/title: Doctoral Position in Earth Observation and Remote Sensing
- The research focuses on investigating/developing generic methods and algorithms in the context of multi-modal synthetic aperture radar (SAR) for the estimation of environmental parameter over arctic regions
- SAR is an active remote sensing technique for estimating a variety of surface features, as e.g. topographic heights, displacements, quantitative bio-physical parameters and has found applications in several domains of the artic region, as e.g. the monitoring of infrastructure and permafrost-related subsidence’s
- Among the most rapid and dramatic changes in the artic regions are retrogressive thaw slumps (RTS)
- These slumps have major impacts by changing ecosystem and hydrological equilibria and furthermore impact the earth system on a global scale by reinforcing climate change with the additional mobilization of organic carbon that was previously stored in the frozen soil
- Due to the remote landscape, only space-born approaches are feasible to monitor them. In recent years several new methods based on optical and radar satellite systems have been developed to detect and monitor thaw slumps on regional scales
- For this doctoral research we are suggesting to investigate TanDEM-X difference digital elevation models to detect RTS on a pan-Arctic, watershed and local scale
- The main data source is coming from TanDEM-X, the German Satellite mission that has acquired a huge amount of data over several years in order to get some insight of the surface processes ongoing and are waiting for their evaluation
Deadline : Open until filled
(13) PhD Degree – Fully Funded
PhD position summary/title: PhD Position in Toxicology
A PhD position is available concerning UV-induced skin aging and enabling biomarker technologies for prevention. The group addresses mechanisms of genome instability of diverse physical and chemical exposures, such as from the environment, lifestyle factors and drugs, as well as cellular metabolism and oxidative stress. In this PhD project, you will expand this research to uncover biomarkers of aging, a process driven by DNA damage. You will use DNA sequencing and related methods to characterize cellular responses to stress and following interventions. Using bioinformatics, these data will be integrated with characteristics such as gene expression and DNA methylation.
Deadline : Open until filled
(14) PhD Degree – Fully Funded
PhD position summary/title: PhD position in predicting Thermodynamics and Processes for Circular Carbon Plastics
The vision of our recently funded ERC Advanced Grant DISC3Lab is to devise a validated discovery loop for sustainable chemical processes, targeting the solvent-based recycling of plastics. Chemical recycling is a key element of a transition towards a chemical industry within the Planetary Boundaries. However, the current process design paradigm is too slow, labor-intensive, and fragmented between disciplines.
We aim to overcome these bottlenecks to accelerate the development of sustainable processes. For this purpose, this project will develop predictive models, ranging from the basis in thermodynamic mixture properties to conceptual process design for chemical plastics recycling, by linking advanced machine learning techniques with high-throughput experimental methods developed by other team members.
Deadline : Open until filled
(15) PhD Degree – Fully Funded
PhD position summary/title: PhD Position in Electrocatalysis and Operando Electrochemical Characterization
Electrochemical energy conversion offers a promising route toward sustainable chemical manufacturing powered by renewable electricity. However, controlling reaction pathways and product selectivity remains a fundamental challenge because electrochemical reactions are governed by complex interfacial processes that are still poorly understood.
In this project, you will investigate how electrolyte environments influence reaction mechanisms in the electrochemical CO2 reduction reaction (CO2RR) and related electrocatalytic processes. We place a strong emphasis on mechanistic understanding and combine advanced operando characterization techniques, including FTIR, DEMS, EQCM, GC, and, where appropriate, synchrotron-based methods, with detailed electrochemical studies to uncover the fundamental principles governing electrochemical interfaces.
Deadline : Open until filled
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(16) PhD Degree – Fully Funded
PhD position summary/title: PhD Position in Light-Driven CO2 Capture and Molecular Systems
Conventional CO2 capture technologies are energy intensive. This project explores an alternative concept in which light is used to control CO2 capture and release using photoresponsive molecular switches, photoacids. The research focuses on understanding how photoacids and related molecular switches influence proton transfer, chemical equilibria, and CO2 capture chemistry, with the goal of developing new approaches for energy-efficient carbon capture.
The project combines molecular synthesis, physical chemistry, spectroscopy, and reaction/process engineering to establish the mechanistic principles governing light-responsive CO2 capture systems.
Deadline : Open until filled
(17) PhD Degree – Fully Funded
PhD position summary/title: PhD position in Automated Laboratory for Plastic Recycling
The vision of our recently funded ERC Advanced Grant DISC3Lab is to devise a validated discovery loop for sustainable chemical processes, targeting the solvent-based recycling of plastics.
This project contributes by developing a Thermodynamic & Process Lab-on-a-chip. We will establish, an automated small-scale experimental platform yielding high-throughput measurements of all fluid properties involved in the design of the targeted recycling processes. An example is the vapor-liquid equilibrium curve for a recyclate mixture to be fractionated through distillation. And the process-level key performance metrics for the involved separations. An example would be the localization of azeotropes, potentially jeopardizing the feasibility of separation through distillation
These data will inform and validate the predictive numerical methods developed by other team members.
Developing sustainable chemical processes requires accurate experimental thermophysical property data. Although experimental data are crucial, they are often scarce because traditional experiments are too expensive, laborious, time-consuming, and error-prone to support rapid process design. In our lab, we tackle these challenges by combining microfluidics and laser spectroscopy to explore complex phenomena such as multi-component phase equilibria, diffusion, and reactions.
Deadline : 7 August 2026.
(18) PhD Degree – Fully Funded
PhD position summary/title: PhD position for developing multi-physics models of planar inductors and transformers
We are looking for highly motivated individuals with an outstanding academic background who are interested in pursuing a PhD in the important and multidisciplinary research area of modelling and optimising planar magnetic devices (inductors and transformers). Compact and highly efficient planar magnetic devices utilising for example PCB based winding structures are key components of many power electronic converters with a low profile, which are required for example for DC-DC converters (PoL-converters) in future data centres or in automotive applications for interconnecting the main drive battery with the 12V auxiliary battery. Planar magnetic devices are also often required in wireless charging/energy transfer systems, where they must be highly compact and efficient.
To push the efficiency and power density limits of such planar magnetic devices, you will develop multi-physics models of planar magnetics. These models will comprehensively describe the (HF) losses in the windings, the parasitic capacitances of the windings, and also the temperature distribution in the winding and also the core. Furthermore, you will develop models for designing the electrical insulation of the winding. Based on these models, you will work on optimising the core and winding geometry, as well as developing advanced cooling concepts for the windings and core. Based on the models and developed concepts, you will then identify technological barriers to power density of planar magnetics.
The developed models should be integrated into optimisation procedures for power electronic converter systems in a computationally efficient manner. This is essential for achieving outstanding system efficiencies and power densities, as well as gaining a competitive edge. The models are also part of an ongoing, wider modelling framework development project at HPE, offering the opportunity to collaborate with other PhD candidates on various modelling aspects.
For verifying the new cooling and winding concepts as well as the developed models, you will design and build different prototypes, including PE converter systems, to operate the planar magnetics under realistic conditions.
Deadline : Open until filled
(19) PhD Degree – Fully Funded
PhD position summary/title: PhD opening in material science of stone conservation
This project aims to establish and validate a novel experimental approach to the study of stone deterioration by leveraging powder-bed 3D printing of stone-like materials. This enables the creation of spatially graded samples mimicking natural stones and their alteration through weathering. Most importantly, this can be done in a repetitive way and with systematic variations of the defect, flaws and inhomogeneities known to play an essential role in stone deterioration.
The specific stone deterioration mechanism that will be investigated is contour scaling, a process of particular concern to molasse sandstones largely used in the Swiss plateau. It leads to loss of cohesion below the exposed surface, at a depth in the range of some millimetres to several centimetres. It leads to highly inhomogeneous properties that our 3D printing approach would be able to mimic for the first time.
The 3D printing will begin using an innovative and large-scale 3D powder bed printer developed by the Chair for Digital Building Technologies, and will be advanced for the purpose of this project by the other PhD student.
Deadline : Open until filled
(20) PhD Degree – Fully Funded
PhD position summary/title: PHD position on Atomic-Scale Structure–Function Relationships in Separation Materials for Sustainable Processes
The aim of this project is to establish a fundamental understanding of how atomic structure governs selectivity, capacity, and stability in materials for CO₂ capture and for the recovery of valuable elements such as lithium and cobalt from complex waste streams. These applications are key to enabling sustainable carbon management and circular resource use.
Deadline : Open until filled
(21) PhD Degree – Fully Funded
PhD position summary/title: PhD student for High-ResolutionTime-Resolved Microstructural Evolution Under Ion Irradiation via TEM
Are you ready to dive into Materials Science?
We are looking for a PhD candidate to work at ER-C (Forschungszentrum Jülich) and LMPT (ETH Zürich) to investigate high-resolution, real-time microstructural evolution in materials under ion irradiation and thus to understand in detail the performance of materials in e.g. future fusion reactors or particle accelerators and in general to understand materials driven far out from equilibrium. In situ TEM and Dynamic TEM (DTEM) are powerful techniques that can be used to reveal microstructure–property relationships in materials under operational conditions, which remains largely unexplored for ion-irradiated materials. Despite decades of research, the fundamental mechanisms governing the sub-nanosecond displacement cascade evolution, and the resulting defect dynamics and irradiation-induced phase transitions are incompletely understood, especially in the presence of realistic external stimuli.
Project Overview
The successful candidate will utilize advanced analytical and high-resolution transmission electron microscopy (TEM)—including collaborations at the ER-C—to directly observe structural changes at the atomic level. Fast scanning calorimetry and synchrotron radiation techniques will be employed to probe the kinetics and thermodynamics of phase transformations. The project will explore how temperature, mechanical strain, and irradiation induce changes in atomic structure and dynamics. The influence of atomic arrangement on stability, transformation kinetics, and crystallization will be systematically investigated.
Deadline : Open until filled
(22) PhD Degree – Fully Funded
PhD position summary/title: PhD position on engineering of machine perfusion platforms for organ and tissue culture
The Macromolecular Engineering Laboratory has a long-standing research program in the engineering of ex situ organ perfusion systems, developed as part of the interdisciplinary Liver4Life project in collaboration with the University Hospital Zürich and the Wyss Zürich Translational Center. Over the past decade, the lab co-developed a normothermic machine perfusion platform that replicates key physiological functions — including pulsatile circulation, oxygenation, dialysis, and hormone and nutrient supplementation — to maintain organs viable outside the body for extended periods. This work culminated in world-first demonstrations of human liver preservation for one week ex situ [Eshmuminov et al. Nat. Biotechnol. 2020] and the first clinical transplantation of a liver treated and recovered in a perfusion machine [Clavien et al. Nat. Biotechnol. 2022]. In parallel, the lab has developed a compact machine perfusion platform for rat liver, enabling fundamental studies of organ metabolism, pharmacology, and perfusion biomarker discovery under controlled ex situ conditions.
Deadline : Open until filled
(23) PhD Degree – Fully Funded
PhD position summary/title: PhD on Quantum Computing × AI for Optimization
You will have the opportunity to work across both ETH Zurich and IBM Research Zurich, benefiting from the synergy between academic and industrial research. The topic of the PhD position sits at the crossroads of quantum computing, machine learning, and combinatorial optimization — an area where some of the most exciting and open questions in the field live. Research directions include:
- Using AI to learn parameters and design circuits for quantum optimization algorithms (e.g., beyond QUBO formulations for QAOA)
- Developing AI-driven methods to discover quantum optimization algorithms
- Generating quantum samples to warm-start classical AI solvers, or to produce quantum-enhanced features and embeddings
The core questions driving this research are fundamental and timely: How can we ensure AI-discovered quantum algorithms are not classical in disguise? How can we generate large-scale, high-quality quantum training data? And how can we combine quantum computing and AI to achieve a measurable, real-world advantage?
Deadline : Open until filled
(24) PhD Degree – Fully Funded
PhD position summary/title: PhD position in Computational earthquake rupture mechanics
At the Professorship of Solid Mechanics (SMEC) in the Institute for Building Materials at ETH Zurich, we aim to understand how materials deform, degrade, break, and ultimately fail. Our research is driven by curiosity about the physical mechanisms that underlie failure and by the ambition to translate this understanding into more reliable and resilient materials and structures. By combining numerical modeling, laboratory experiments, and theoretical analyses, we seek to link microscopic processes with the macroscopic behavior of both engineering and natural systems and develop predictive tools for mechanical failure.
Our team is highly interdisciplinary and international, bringing together researchers with backgrounds in materials science, mechanics, and applied physics. We work across a broad range of topics, including the mechanics of particle systems (colloidal and granular), architected and topologically interlocked materials, the mechanics of fragility in collagen, the mechanics of earthquakes, fracture of soft materials, and modeling failure in multiphysical processes such as corrosion-driven degradation of concrete. What unites these efforts is a shared curiosity about why complex materials fail and a commitment to developing new concepts, experiments, and models that advance our understanding of failure mechanics.
We are seeking a motivated, innovative PhD student with a background in computational mechanics to work on a project in computational earthquake rupture mechanics. The project will combine numerical method development and theoretical analysis to investigate how heterogeneous stress states and nonlinear near-fault processes influence earthquake rupture propagation, arrest, and earthquake-size statistics.
Deadline : Open until filled
(25) PhD Degree – Fully Funded
PhD position summary/title: PhD Position in AI-Supported Resilience Assessment of Climate-Neutral Transport Infrastructure
This doctorate aims to advance the state of the art in simulation-informed, AI-supported resilience assessment for transport infrastructure. Working closely with academic, industry, infrastructure, and public-sector partners across Europe, the candidate will contribute to the development of stress-testing frameworks, surrogate modelling algorithms, and decision-support tools for climate-resilient and sustainable transport systems. The candidate’s core tasks will include:
- Developing AI-supported surrogate models for infrastructure stress testing
- Linking surrogate models with agent-based and network-based simulations
- Assessing infrastructure interdependencies and recovery constraints
- Supporting resilience assessment across asset, corridor, and network scales
- Contributing to cost-benefit and decision-support analysis
- Addressing uncertainty, reliability, and explainability
- Integrating results with digital-twin and monitoring data
- Collaborating with the European project consortium
Deadline : Open until filled
(26) PhD Degree – Fully Funded
PhD position summary/title: Marie Skłodowska-Curie Doctoral Training Network – Coupled Problems for Decarbonization in Industry and Power Generation (COMBINE)
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Project title: High resolution experimental measurements of vibrations in rods and rod bundles using ensemble average high-resolution gamma tomography measurements.
This project focuses on the development of advanced tomographic techniques to evalute vibrations in rod bundles. The research will leverage our in-house, state-of-the-art, high-resolution gamma tomography system.
Deadline : Open until filled
(27) PhD Degree – Fully Funded
PhD position summary/title: PhD position in Multi-State Programmable Robotic Matter
Modern robots increasingly rely on sophisticated mechanisms, such as modular architectures, reconfigurable linkages, deployable structures, and variable-stiffness components, to adapt their bodies to different tasks and environments. However, such adaptability is often achieved by changing geometry, connectivity, or control strategies, while the underlying material properties remain largely fixed.
This project explores a more fundamental route: robotic matter whose constitutive properties can be programmed on demand. By enabling mechanical modulus, viscoelastic response, and flow behavior to be reconfigured within a single continuous body, we aim to move beyond adaptive hardware architectures towards materials that actively participate in robotic function. The long-term vision is to create robots that can tune their physical embodiment through the programmable properties of matter itself.
Deadline : Open until filled
(28) PhD Degree – Fully Funded
PhD position summary/title: PhD Position in Infrastructure Intervention Effectiveness Analysis for Road Safety
The new EU Horizon project advances safe active mobility uptake and research by introducing a human-centred, evidence-based approach that integrates actual and perceived safety for pedestrians, cyclists, and micromobility users. Moving beyond conventional crash-focused approaches, it captures near-misses, dynamic interactions, and embodied safety experiences that shape behaviour and mode choice. The project combines multi-source traffic, infrastructure, vehicle, and health data with immersive eXtended Reality (XR) experimentation and explainable Artificial Intelligence to analyse safety-critical situations that are rare, underreported, or ethically impossible to observe in real traffic. Explainable AI ensures transparency and interpretability, supporting trust, transferability, and policy relevance. The project translates these insights into harmonised assessment methodologies, predictive models, and validated indicators, enabling robust evaluation and comparison of regulatory, infrastructural, technological, and behavioural interventions across Safe System Approach stakeholders. Special focus is placed on interactions between users with differing masses and speeds, including e-bikes, e-cargo bikes, and e-scooters, for both personal mobility and urban logistics. Large-scale pilots in four European cities validate methods in real traffic, support cross-city learning, and ensure applicability under diverse safety, infrastructure and cultural conditions. Implemented by a multidisciplinary consortium bridging engineering, behavioural science, XR, AI, urban planning, and policy, the project delivers actionable, standardised guidance that accelerates safer, more inclusive active and micromobility systems across Europe.
Planning safe urban transport systems is inherently complex: interventions last for decades, have widespread impacts, require significant investment, must fit within constrained urban spaces, and must satisfy ever-changing, diverse user needs. Modern data collection and analysis methods are well-suited to capture these complexities by utilizing high-resolution data (e.g., high-frequency measurements from on-board vehicle sensors and computer-vision imagery) to provide near real-time insights. However, it is not yet clear how to effectively operationalize this data to identify high-risk areas, propose targeted infrastructure interventions, and measure their success – largely due to the relative novelty of these data sources, rapid vehicle advancements, and evolving urban mobility patterns.
Deadline : 30 July 2026
(29) PhD Degree – Fully Funded
PhD position summary/title: PhD Position in Vehicle Sensor and Remote Sensing Analysis for Road Safety
The new EU Horizon project advances safe active mobility uptake and research by introducing a human-centred, evidence-based approach that integrates actual and perceived safety for pedestrians, cyclists, and micromobility users. Moving beyond conventional crash-focused approaches, it captures near-misses, dynamic interactions, and embodied safety experiences that shape behaviour and mode choice. The project combines multi-source traffic, infrastructure, vehicle, and health data with immersive eXtended Reality (XR) experimentation and explainable Artificial Intelligence to analyse safety-critical situations that are rare, underreported, or ethically impossible to observe in real traffic. Explainable AI ensures transparency and interpretability, supporting trust, transferability, and policy relevance. The project translates these insights into harmonised assessment methodologies, predictive models, and validated indicators, enabling robust evaluation and comparison of regulatory, infrastructural, technological, and behavioural interventions across Safe System Approach stakeholders. Special focus is placed on interactions between users with differing masses and speeds, including e-bikes, e-cargo bikes, and e-scooters, for both personal mobility and urban logistics. Large-scale pilots in four European cities validate methods in real traffic, support cross-city learning, and ensure applicability under diverse safety, infrastructure and cultural conditions. Implemented by a multidisciplinary consortium bridging engineering, behavioural science, XR, AI, urban planning, and policy, the project delivers actionable, standardised guidance that accelerates safer, more inclusive active and micromobility systems across Europe.
Planning safe urban transport systems is inherently complex: interventions last for decades, require significant investment, must fit within constrained spaces, and must satisfy ever-changing user needs. Modern data collection methods – such as high-frequency on-board vehicle sensors and computer-vision imagery – are well-suited to capture these complexities via near real-time, high-resolution insights. However, collecting, processing, and operationalizing these big data volumes is a challenge due to heterogeneous data structures and heavy computational demands.
Deadline : 30 July 2026
(30) PhD Degree – Fully Funded
PhD position summary/title: Doktorand:in in Rechtswissenschaften (m/w/d)
Die Doktorandenstelle ist auf die empirische Erforschung von KI-gestützten Entscheidungshilfen in der Justiz ausgerichtet. Die doktorierende Person wird die Auswirkungen solcher Systeme auf die richterliche Praxis untersuchen – durch die Konzeption und Durchführung von Feldexperimenten in Zusammenarbeit mit Schweizer Gerichten sowie deren rechtliche und institutionelle Evaluation.
Deadline : Open until filled
About ETH Zurich, Switzerland- Official Website
ETH Zürich is a public research university in the city of Zürich, Switzerland. Founded by the Swiss Federal Government in 1854 with the stated mission to educate engineers and scientists, the school focuses exclusively on science, technology, engineering and mathematics. Like its sister institution EPFL, it is part of the Swiss Federal Institutes of Technology Domain, part of the Swiss Federal Department of Economic Affairs, Education and Research.
In the 2021 edition of QS World University Rankings, ETH Zurich was ranked 6th in the world, placing it as the second-best European university after the University of Oxford. In the 2020 QS World University Rankings by subject, it ranked 4th in the world for engineering and technology (2nd in Europe) and 1st for earth & marine science. Also, ETH was rated 8th in the world in the Times Higher Education World University Rankings of 2020.
The university is an attractive destination for international students thanks to low tuition fees of 809 CHF per semester, PhD and graduate salaries that are amongst the world’s highest, and a world-class reputation in academia and industry. There are currently 22,200 students from over 120 countries, of which 4,180 are pursuing doctoral degrees.
As of November 2019, 21 Nobel laureates, 2 Fields Medalists, 2 Pritzker Prize winners, and 1 Turing Award winner have been affiliated with the Institute, including Albert Einstein. Other notable alumni include John von Neumann and Santiago Calatrava. It is a founding member of the IDEA League and the International Alliance of Research Universities (IARU) and a member of the CESAER network.
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