Postdoctoral Fellowship (19) at Delft University of Technology (TU Delft), Netherlands

Platform-mediated cycling logistics has become a structural component of urban freight in the Netherlands, yet the coordination between platform operations and municipal safety planning remains underdeveloped. The PROSPER project, funded under TKI Dinalog, develops decision-support methods to inform this coordination, in collaboration with platform, municipal, and academic partners. The postdoctoral position offered here constitutes the methodological core of the project.

The work combines empirical analysis of courier movement data, spatial modelling of urban risk exposure, and simulation-based evaluation of routing and policy strategies. The successful candidate will lead the technical research, contribute to publications in leading journals in transportation and operations research, and help shape the translation of the resulting methods into outputs developed with the project’s industrial and municipal partners.

The position is hosted by the ORBIT Lab (Operations Research and Behavioural Informatics in Transportation) at the Faculty of Technology, Policy and Management, under the supervision of Dr. Yousef Maknoon.

Are you an ambitious researcher in applied mathematics or machine learning who wants to develop new methods, apply them in practice, and contribute to real-world impact? We invite applications for a postdoctoral position in the Numerical Analysis group at the Delft Institute of Applied Mathematics (DIAM), part of the Faculty of Electrical Engineering, Mathematics & Computer Science (EEMCS) at TU Delft. The position is embedded in the NWO Perspectief Project FAB4FUTURE, an interdisciplinary programme that develops next-generation biofabrication technologies for regenerative medicine and sustainable food production. A central goal is to create an artificial intelligence (AI)-driven toolbox for the bioprinting process, enabling accurate, efficient, and scalable control of complex biofabrication workflows.

The project brings together a broad consortium of academic partners, including Delft University of Technology, Maastricht University, UMC Utrecht, Utrecht University, and Zuyd University of Applied Sciences. It further includes industrial partners such as Axolotl, Demcon, Mosa Meat B.V., Poietis, RDInnovation, ReGEN Biomedical B.V., Scinus, and Xolo. Societal partners include Cellulaire Agricultuur Nederland, Dutch CardioVascular Alliance, Good Food Institute Europe, and Stichting AVS Proefdiervrij, ensuring strong links between fundamental research, technological development, and real-world application.

As a postdoctoral researcher, you will develop scientific machine learning methods for modeling and control of biofabrication processes, with a focus on cell and material deposition in soft, deformable biological systems. You will design approaches based on state-of-the-art techniques, such as convolutional neural networks, transformer models, operator learning, and optimization and control methods, while embedding morphoelastic and biomechanical models into the learning process. Your work will contribute directly to the AI-driven toolbox through predictive modeling, parameter optimization, and real-time control of the bioprinting process. You will apply and validate these methods on experimental data in close collaboration with leading biofabrication groups at UMC Utrecht and Maastricht University. This includes the development of surrogate models, inverse modeling, and integrated learning and control strategies. You will leverage experimental data from these partners while contributing insights to refine and improve biofabrication hardware and protocols.

Software is at the core of modern society — from communication networks and financial systems to medical devices and transport infrastructure — and ensuring that it behaves correctly is both essential and notoriously difficult. Proof assistants such as Agda and Rocq (formerly Coq) make it possible to construct mathematically rigorous, machine-checked guarantees about software behaviour, but applying them to programs written in mainstream languages remains a significant challenge. This is especially true for software that exhibits cyclic behaviour: programs with loops, recursive data, or continuous interaction with their environment, which require a careful interplay of inductive and coinductive reasoning to verify.

In this postdoc position, you will work at the intersection of proof assistants and modern systems programming. Your central task is to design and prototype a way to verify Rust programs — and in particular programs with cyclic structures — by translating them, together with logical annotations supplied by the developer, into a proof assistant where their correctness can be machine-checked. The aim is not to build yet another verification tool from scratch, but to make state-of-the-art research on inductive-coinductive type theory genuinely usable for Rust developers. You will work closely with a parallel PhD project on first-class coinduction in proof assistants, helping to refine the underlying type theory and putting it to the test on realistic Rust programs.

This position is part of the NWO-XL consortium project Cyclic Structures in Programs and Proofs: New Harmonies in Software Correctness by Construction, a collaboration between five Dutch universities (TU Delft, Groningen, Leiden, Nijmegen, and Twente) which brings together expertise in formal logic, programming language theory, concurrency, and proof assistants. You will be based at TU Delft in the Programming Languages group, supervised by Jesper Cockx, and will collaborate closely with the other PhD students, postdocs, and senior researchers in the consortium. Within the wider project, your work forms a bridge between foundational research on coinductive reasoning and its practical application to real programs, and as such will play a key role in demonstrating that the consortium’s theoretical advances translate into concrete tools that practitioners can use.

You will have significant freedom to shape the technical agenda, publish your findings at leading venues (such as POPL, ICFP, OOPSLA, ITP, and CPP), and contribute to the open-source tools developed within the consortium. You will also be encouraged to spend time at one of the partner universities and to engage with the broader national and international research community via the NetTCS network and consortium-organised workshops and schools.   

We are looking for a highly motivated PhD candidate to join the Quantum Spintronics (QuSpin) Lab in the Department of Quantum Nanoscience, TU Delft. The project is aimed at developing spintronic devices based on graphene and van der Waals heterostructures. The project spans from building nano-constrictions and addressing spin-polarized quantum point contacts and quantum dots to functional topological spintronic devices based on quantum spin Hall currents. Your research will focus on understanding and controlling spin-polarized quantum transport at the nanoscale.

Your main tasks will include:

• Fabrication of graphene-based nanoelectronic devices using state-of-the-art cleanroom facilities
• Assembly of van der Waals heterostructures using dry-transfer techniques
• Low-temperature transport measurements
• Data analysis and modelling of quantum transport measurements
• Close collaboration with other researchers within the department, and interaction with leading experimental groups within the Dutch quantum ecosystem
• Attendence and presenting in national/international conferences

Nature-based Solutions (NBS) are strongly advocated to reduce societal risks, e.g. by mitigating floods, cooling urban areas, and improving water regulation, especially in the context of increased climate extremes and growth of urbanised areas. Besides disaster risk reduction, NBS can also provide benefits linked to human wellbeing and health. However, significant uncertainties and knowledge gaps remain regarding how NBS will perform under future climate extremes and within specific socio-economic contexts, and how these solutions translate into community health outcomes.

The nexus of water and health is the interface between the biophysical system of water (ecosystem), the socio-economic and governance dimensions of water systems, and human health. Traditionally, work in this nexus focuses on hydrological pathways of waterborne and water-related diseases and on water treatment technologies that ensure safe water for human use. In our project, we expand this perspective by explicitly considering the combination of heatwaves and flood events in urbanised areas with NBS measures intended to mitigate their impacts.

For instance, the global rise in hydrometeorological extremes leads to critical health impacts through heatwaves, floods, and droughts, as well as the spread of waterborne pathogens. These stresses on water quantity and quality require the hydrological community to deepen the understanding of the underlying mechanisms linking pathogens to human exposure risks, which can be systematically evaluated using quantitative microbial risk assessment (QMRA) frameworks, and to define the scale, magnitude, and duration of extreme hydrometeorological events. This need becomes more urgent when applying NBS to mitigate the consequences of hydrometeorological extremes, particularly in urbanised areas. Strong hydrological knowledge is essential to be better prepared for disruption of health services and critical infrastructure.

This three-year Postdoc project is part of the PDPC initiative (Pandemic Disaster Preparedness Centre), a convergence initiative of TU Delft, Erasmus University and Erasmus Medical Centre. We will target efforts in leveraging advances in hydrology and meteorology (including process-based understanding and increased modelling capabilities) and integrate these with public health intervention strategies (with EUR and EMC) at scales relevant to communities and cities. The Postdoc will play a pivotal and initiating role in inter- and transdisciplinary research on the Water–Health Nexus, with a particular focus on the unknown consequences and potential health benefits and drawbacks of NBS. Moreover, the research can include (i) improving the efficiency of water, sanitation, and hygiene practices, (ii) using knowledge of the dynamic evolution of hydrological connectivity to predict pathogen dispersal and resulting human exposure risks, (iii) applying QMRA to evaluate human exposures and health risks under different hydrometeorological and NBS scenarios.

We are seeking a highly motivated Postdoctoral Researcher to join the ongoing research activities within the framework of the CRESYM organization. The position is connected to the Harmony project, funded by CRESYM, TenneT, and Swissgrid, and focuses on advanced coordinated optimal control and optimization of integrated AC-MTDC power systems, with a particular emphasis on OPF-related methodologies for future transmission networks.

The candidate will work on the modeling, analysis, and simulation of AC–MTDC systems, including the development of optimization and control frameworks that enable secure, efficient, and flexible grid operation. Depending on the candidate’s expertise and interests, the research may include topics such as:

• Optimal control of AC–MTDC systems with high penetration of renewable energy sources
• Dynamic and security-constrained optimal power flow
• Multi-timescale operation and control of hybrid AC/DC grids
• Real-time and predictive control methods for future power systems

This is a full-time postdoctoral appointment with close collaboration opportunities with industrial partners including TenneT and Swissgrid. The position offers access to advanced simulation and experimental infrastructure, including RTDS platforms, HVDC and DC grids laboratories, and wide-area monitoring and protection systems.

Human behavior understanding is becoming an essential component across a range of application areas, including education and healthcare. As these systems are increasingly deployed in high-stakes environments, there is a growing need for machine learning models that are not only accurate but also transparent and explainable. This project focuses on the automatic perception of situational interest and state epistemic curiosity, which are motivational factors that influence human learning. While multimodal systems are already being developed to monitor students’ physical and cognitive engagement, current approaches offer limited interpretability and often do not provide meaningful explanations of their predictions to downstream users.

A central aim of this project is to investigate how  vision language models and/or multimodal large language models, among other technologies, can support explanation in situated conversational settings. We view explanation not as a static output, but  as an interactive process that evolves through human-human and human-AI dialogue, where meaning emerges through grounding, clarification, and feedback. A possible application domain for this work is augmented/mixed reality learning environments  which require real time perception and explanation within contexts.

Possible research directions include mechanistic interpretability of multimodal models, concept based explanations grounded in affect and behavior theory, and multimodal sensing from vision, audio, and physiological signals. The project also will focus on developing interactive systems that support real time dialogue and user questioning, with deployment in immersive environments.

We are pleased to announce one posdtoc position within the newly awarded ERC Advanced Grant LeakingOceans. This interdisciplinary project aims to reveal how the hidden oceans of icy moons, such as Enceladus, Europa and Ganymede, interact with their surfaces. Although these oceans are unreachable today, they leak through the moons’ icy crust, providing natural access to their composition and dynamics. Previous missions have shown that subsurface oceans reach the surface through several processes, and LeakingOceans aims to understand, quantify and detect these leaks and the ocean materials they deliver.

We are looking for a highly motivated postdoctoral researcher to join the EuRadCA project, a German-Dutch collaboration focused on advancing radar-based quantitative precipitation estimation across Europe. This new position offers a unique opportunity to work at the interface of radar hydrometeorology, geospatial analysis, and machine learning, leveraging large-scale European radar datasets such as OPERA.

The Embedded Systems Group at TU Delft invites Expressions of Interest (EoI) from outstanding international researchers who wish to apply for the Marie Skłodowska-Curie Actions (MSCA) Postdoctoral Fellowship.

This prestigious fellowship offers an exceptional opportunity to advance your research career through advanced training, international mobility, and collaboration within a world-leading research environment.

As postdoctoral researcher, you will lead the development of the UrbanHealthTwin digital twin platform, integrating climate, hydrological, epidemiological, biological, and urban-system models into an interactive and explainable decision-support environment for climate adaptation and public health planning.

You will coordinate digital twin activities across the wider international consortium and help ensure that the platform aligns with both scientific objectives and practical stakeholder needs. You will also investigate existing digital twin initiatives in Rotterdam and explore opportunities to connect and build upon ongoing city-scale developments.

Coastal and offshore infrastructure systems are increasingly exposed to changing environmental conditions, sea level rise, and sequences of extreme events. Understanding how these changes affect their reliability and resilience is essential for supporting adaptation strategies, risk-informed design, and long-term decision-making.

TU Delft coordinates the Horizon Europe project EU-INTERCHANGE, which develops climate services, datasets, and tools to assess the impacts of climate change on European regional and coastal seas. The project will generate state-of-the-art hindcast and future projection datasets of marine environmental parameters, including waves, water levels, ocean conditions, storm surge information, and climate-change scenarios. These datasets provide a unique opportunity to assess how present and future environmental conditions may affect the reliability of coastal and offshore infrastructure systems.

Within this position, you will develop and apply reliability and risk analysis frameworks for coastal and offshore engineering systems, with specific attention to the combined effects of marine environmental loads, sea level rise, climate change, and system deterioration. The work will contribute to the identification of coastal risks and to the development of approaches that support coastal resilience, protection, and adaptation.

You will use advanced statistical and probabilistic methods to assess failure probabilities and reliability under present and future climate conditions. This may include Monte Carlo methods, multivariate statistical approaches, Bayesian networks, vine copulas, or other suitable methods to describe the dependence between environmental variables, infrastructure components, and possible cascading system effects.

The activities you will be responsible for will combine the processing and analysis of large environmental datasets with the development of probabilistic models for engineering reliability assessment. You will derive relevant environmental loading conditions, quantify uncertainties, and translate climate projections into reliability indicators and risk-based information that can support coastal infrastructure planning and adaptation. As a final stage, the developed reliability framework will be embedded in the EDITO portal, ensuring that the project outputs can be accessed and used within a wider digital environment for marine and coastal data services.

At TU Delft, you will contribute to research that addresses one of the most pressing challenges in the maritime sector: reducing fuel consumption and emissions while maintaining safe and efficient operations. Within this project, you will develop advanced modelling approaches that combine artificial intelligence with physical insights to better understand and predict vessel performance under real operating conditions such as variable weather and progressive hull fouling. Within this project, you will develop advanced modelling approaches that combine artificial intelligence with physical insights to better understand and predict vessel performance under real operating conditions such as variable weather and progressive hull fouling. The position is embedded in the Maritime AI Innovation Lab, a collaborative initiative involving TU Delft, MARIN, Damen, RH Marine, SafeSystems, Spliethoff, and Ocean AI, and aims to translate cutting-edge research into practical tools for cleaner and more efficient ship operations.

The maritime industry is undergoing a major transition towards more sustainable operations, driven by stricter environmental regulations and global decarbonisation targets. Digitalisation and AI play a key role in this transition, enabling more accurate predictions of fuel consumption, improved maintenance strategies, and optimised vessel performance. The long-term goal of this research is to support the maritime sector’s transition to lower-emission and more energy-efficient operations by enabling reliable, data-driven decision support for vessel performance monitoring, maintenance planning, and operational optimisation. By combining domain knowledge with AI, the project contributes to the development of trustworthy digital tools that can be deployed in real-world maritime settings and help accelerate decarbonisation across the industry.

(18) Postdoctoral Fellowship Position

Postdoc Fellowship Position summary/title: Postdoc AI for Tabular and Temporal Data

Deadline : 19 Jun 2026

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At TU Delft, you will contribute to a transformative national initiative: “Groeien met Groen Staal (GGS)” programme. The project aims to advance green steel production via hydrogen-based direct reduced iron (H-DRI). Within this ambitious project, you will focus on the atomistic mechanisms governing carburization, wetting and catalytic graphitization of carbonaceous materials. Using advanced atomistic modelling techniques, you will unravel iron-carbon interactions at the electronic and atomic scale delivering insights directly relevant to the sustainable steelmaking route.

Team Dey within the Computational Materials Science section at TU Delft is actively involved in Theme II: Production within the GGS programme. This initiative pushes beyond current state-of-the-art technologies, exploring alternatives to conventional blast furnace (BF) and basic oxygen furnace (BOF) routes. A promising pathway combines hydrogen-based direct reduction (DR) with reduced electric furnace technologies (REF) and the BOF. Within this context, this position focuses on understanding the atomistic mechanisms that underpin these processes, providing fundamental insights into carbon behaviour, interfacial interactions and material transformations that are essential for optimising next-generation steelmaking routes.

Where experimental work within the project focuses on process development and validation, this position addresses the underlying governing atomistic mechanisms. A critical factor in the REF route is the role of carbon, particularly in relation to graphitization, wetting behaviour and interfacial interactions. Understanding these processes at the atomistic level is essential to control carburization and the melting phenomena. Your work will provide the theoretical foundation that supports and complements experimental efforts within the GGS programme.