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

Steel production is estimated to account for approximately 7% of global CO₂ emissions. Achieving sustainable steel production requires a combination of green hydrogen usage, full recycling of scrap steel, and the electrification of processes. The adoption of new sustainable process technologies will necessitate changes in heat treatment methods, particularly in heating systems. These changes will primarily affect furnace atmospheres, which in turn influence surface reactions during steel processing.

Additionally, the increased use of recycled steel introduces trace elements—known as tramp elements— alters steel composition. This has important implications for surface quality, surface-dependent mechanical properties, and product manufacturability.

This project aims at understanding the oxidation processes occurring in reheating furnaces, where the as-cast steel is heat-treated to optimise its mechanical properties for entering the rolling mills.

The transition from fossil-based to green furnace atmospheres will lead to an increase in water and CO2 content in the furnace thereby affecting the kinetics of growth and morphology of the oxide layer. In green steelmaking it is also aimed at reusing of steel from scrap. The introduce tramp elements is expecting to influence growth kinetics, type and morphology of the formed oxides. Important features of the oxide layer for further steps in steelmaking are the porosity, the layer thickness (loss of iron) and the adhesion of the oxide to the substrate, a key factor for the descalability.

Oxidation also causes steel surfaces to change from being reflective metals to absorbing dielectrics. The thickness and morphology of the oxide determine the emissivity, that affects both the heat transfer and the temperature profile during processing.

The PostDoc position is part of research of prof. Bernd Rieger and prof. Sjoerd Stallinga, in which we target (super-resolution) microscopy image processing and analysis method development. The position builds on the lab’s track-record in the field of computational imaging techniques for super-resolution microscopy and image analysis for biological imaging in general.

The position is part of the Netherlands BioImaging Infrastructure (http://eurobioimaging.nl) which is a nation‐wide multi‐sited facility that enables breakthrough in cell/life science across experimental models and imaging modalities. The Delft work is placed in the microscopy innovation node, with biological collaborators at Utrecht University (prof. Lukas Kapitein) and Erasmus Medical Center Rotterdam (prof. Adriaan Houtsmuller and the imaging center team). The PosDoc in encouraged to interact with these collaborators defeining research questions regarding super-resolution microscopy method development. We are in particular interested to build upon our track record of Point Spread Function fitting for single molecule localization microscopy. Here wer combine computational, algorithmic aspects, such as Parallelization, Fisher Information and Maximum Likelihood Estimation, with optical theory.

This is an opening for a two-year postdoc position in the NWO algosoc Project, located with our research group at TU Delft. We invite applications from candidates who are interested in taking a technically informed political economic approach to empirically researching the expansion of computational infrastructures into public institutions. We are especially interested in candidates who want to move the needle from tech reformist projects towards reimagining other socio-technical-economic futures that respect public values and interests in the making. This position is an exciting opportunity for scholars who like to do impactful research in transdisciplinary settings.

We are looking for a passionate postdoctoral researcher to join us at the Bosco group and advance theoretical research in solid-state quantum computation, with a focus on spin-based platforms. Our team is actively engaged to advancing the frontiers of both fundamental physics research and practical device development, bridging the gap between fascinating theory and real-world applications.

Our current research in quantum computing spans several exciting directions, and you will have the opportunity to contribute to all of them.

• Quantum Computation with spin qubits: You will have the opportunity to delve into the theoretical aspects of solid-state quantum computing in spin qubits platforms. This includes gaining expertise in device physics, microscopic modelling of different materials, and quantum information theory.
• Hybrid Super-Semiconducting Systems: You will contribute to pioneering research in hybrid super-semiconducting systems for quantum computing applications, particularly in germanium and silicon. This research direction involves for example the coupling of spins to microwave resonators and circuit quantum electrodynamics (circuit QED) with Andreev spin qubits.
• Scaling Up Quantum Computers: You will actively contribute to advancing the field toward practical quantum computation solutions.

Good hearing is essential for active participation in society. Hearing impairments can lead to various challenges, from frustration and social isolation—such as difficulties understanding speech in noisy environments—to serious safety risks, like the inability to localize sound sources in traffic.

Hearing aids help mitigate hearing loss by enhancing speech intelligibility, improving sound localization, and increasing environmental awareness. However, most existing hearing aid algorithms optimize for only one of these objectives at a time, often at the expense of the others. To enhance hearing aid performance across diverse scenarios, we aim to develop innovative algorithms that dynamically balance the improvement of speech intelligibility, sound localization, and environmental awareness, dependent on the context. 

To tackle this challenge, we are seeking a postdoctoral researcher with expertise in speech signal processing, optimization, machine learning, and models of hearing and perception.

This is a full-time, three-year position within the audio and speech processing team of the Signal Processing Systems (SPS) Section. In the SPS section, we conduct research in array signal processing, graph signal processing, convex optimization, distributed processing, machine learning, and tensor analysis. Our applications span various domains, including audio and acoustics, wireless communication, radio astronomy, biomedical signal processing, and distributed sensing from space.

We seek a postdoctoral researcher to develop and optimize surface functionalization strategies for optical chips, focusing on molecular recognition systems capable of operating in extreme space environments.

• Surface Chemistry Development: Design and optimize chip surface chemistry for target biomolecules including amino acids, nucleobases, and lipid biomarkers. Explore multiple surface modification strategies including Poly-L-lysine, silane-based approaches, and novel functionalization techniques.
• Molecular Recognition Elements: Characterize the performance of various receptors, ranging from Aptamers to Molecularly Imprinted Polymers (MIPs), optimized for specificity, affinity, binding characteristics, and photonic chip integration.
• Establishing laboratory facilities: Lead the installation of new measurement equipment and setup new lab facilities and protocols appropriate for Life Marker Chip studies. Supervise student using these new facilities.
• Space Environment Testing: Support and supervise comprehensive characterization studies including thermal cycling stability, radiation exposure effects, and long-term vacuum stability of functionalized chips.
• Reporting: Communicate findings to project partners with diverse backgrounds (e.g., space systems engineering, astrobiology, microfluidics) to support the development of a prototype LMCOOL instrument for space operation. Help outline protocols and methodologies for operation in planetary environments. Disseminate findings in the form of project reports, research papers, and at conferences.  

We are seeking for a motivated Postdoctoral Researcher to join the group of Dr. Viatcheslav Dobrovitski to do the research on the dephasing of entangled states of spin and photonic qubits in quantum networks. The postdoc will play an important role in advancing the theoretical understanding of decoherence and its impact on scalable quantum technologies.

As a Postdoctoral Researcher you will :

• Conduct research on the decoherence of entangled spin qubits in semiconductors (≈15%).
• Conduct research on the dephasing of entangled photonic qubits in quantum networks (≈85%).
• Perform analytical and/or numerical studies and analyse results.
• Prepare and submit 1–2 scientific publications.

You will be part of a collaborative and supportive research environment at the heart of the Delft University campus. Our team values diversity and fosters an inclusive, international culture.

Dexterous manipulation represents one of robotics’ most fundamental unsolved problems. While robots excel at precise positioning and heavy lifting, they fail at tasks humans perform unconsciously – sliding a glass without spilling, rotating a screwdriver with proper torque, or coordinating fingers to tie shoelaces. This gap has profound implications for manufacturing, healthcare, domestic assistance, and applications where robots must interact delicately with the physical world.

Current robotic systems rely primarily on vision and force sensors, missing the rich tactile information that enables human dexterity. Our lab pioneered a new tactile sensor technology (best student paper award at ICRA 2025) that offers unprecedented spatial resolution and sensitivity, providing robots with meaningful tactile sensing. This position represents a unique opportunity to leverage rich tactile information for dexterous manipulation.

Your role and impact as postdoctoral researcher, you’ll lead the research in leveraging our tactile sensor technology to control grippers and multi-finger hands. Your primary mission involves developing control strategies that enable robots to perform complex manipulation through tactile feedback. You’ll focus on three critical aspects of the problem: sliding motion control, rotation strategies for precise tool manipulation, and multi-finger coordination for more complex tasks.

We expect that you will blend theoretical approaches with hands-on experimentation, as well as collaborate with PhD candidates. You’ll design experiments using our robotic platform, analyze tactile data patterns, implement real-time control algorithms, and validate performance across diverse scenarios.

Computation-in-Memory (CIM) is being investigated for energy-efficient artificial intelligence (AI) applications. However, this new computing paradigm faces various design challenges in terms of design and technology challenges, application mapping and reliability issues. Thus, there is a growing demand for efficient and reliable digital CIM-based neuromorphic system design which includes techniques such as reliability-aware mapping and optimization techniques as well as high-quality, and cost-effective mitigation schemes that can guarantee efficient and reliable operation.

• The Computer Engineering (CE) section of the Quantum & Computer Engineering (QCE) department is looking for a highly motivated PostDoc candidate who wants to work on efficient and reliable digital CIM-based AI accelerator design for future neuromorphic accelerators. The candidate will work on the following:
• Designing efficient digital CIM architecture using advanced CMOS nodes
• Develop system architecture for digital CIM accelerator
• Develop efficient mapping schemes of AI workloads
• Identifying the reliability failure mechanisms of digital CIM architectures.
• Developing solutions to address the reliability issues.

Various emerging device technologies and computing paradigms are being investigated for energy-efficient artificial intelligence (AI) applications. Some examples are RRAM or STT-MRAM collectively known as memristor-based Computation-in-Memory (CIM) accelerators. However, these new technologies and computing paradigms face various design challenges in terms of design and technology challenges, application mapping and reliability and non-ideality issues. Thus, there is a growing demand for efficient and reliable memristor CIM-based neuromorphic system design which includes techniques such as reliability-aware mapping and optimization techniques as well as high-quality, and cost-effective mitigation schemes that can guarantee efficient and reliable memristor-based CIM operation for AI workloads.

The Computer Engineering (CE) section of the Quantum & Computer Engineering (QCE) department is looking for a highly motivated PostDoc candidate who wants to work on efficient and reliable analog CIM-based AI accelerator design for future neuromorphic accelerators. The candidate will work on the following:

• Designing efficient analog CIM architecture using emerging memristive devices
• Develop system architecture for analog CIM accelerator
• Develop efficient mapping schemes of AI workloads
• Identifying the non-ideality issues of analog CIM architectures.
• Developing solutions to address the non-ideality issues.

The path to sustainable aviation relies on innovative, efficient production technologies. Thermoplastic composites, combined with novel material architectures, are central to this transition, enabling recyclable, lightweight structures. Research now targets energy-efficient, automated processes and machinery to deliver high-quality components for the next generation of aircraft.

This postdoctoral project aims to experimentally characterize the temperature distribution and heat transfer during the heating of microscopic aggregates of thermoplastic cut tapes, with the goal of accelerating process development for circular materials. You will generate valuable multi-scale data (micro- to mesoscopic) relevant to the manufacturing of aerospace thermoplastic composites, and will play a key role in linking experiments with simulations from a companion PhD project. The work will involve close collaboration with industrial partners and is expected to provide both fundamental insight and practical guidance for advanced composite processing.

The project is conducted at the department of Aerospace Materials and Structures (ASM) of the Faculty of Aerospace Engineering (AE) and will be embedded within the Lightweight Composites and Structures project of the “Luchtvaart in Transitie” (LiT) initiative, which brings together a variety of companies and knowledge institutes to develop materials, production technology and structures for ultra-efficient aircraft. This large-scale initiative of the Dutch growth fund is aligned with the mission of the ASM department to make aviation and spaceflight more sustainable by developing novel structures and materials that have a smaller environmental footprint by using less energy and enabling easier recycling, while simultaneously researching how to make structures smarter, lighter and safer, to support the transition to new sources of energy.

Negotiations involve understanding and managing interpersonal relationships, motivations, and conflicts. While there is existing research on building negotiating agents, there remains a gap to integrate situated understanding of human behavior and how these affect the negotiation process. In this project, you will contribute to the research in building more effective, adaptive, and ethically aware decision-making support in multi-stakeholder negotiations.

This project is part of work package 5 of the Red&Blue consortium (https://redblueclimate.nl/) that focuses on climate change in the built environment.and aims to co-design and co-develop an Integrative Forum (IF) to facilitate two-way transdisciplinary exchange and dialogue on climate risk management. Within that scope of climate discussions and negotiation, you will develop methods to analyze human behaviors and decision-making processes using multimodal data (text, audio, and/or video), particularly for in-the-wild settings.

The technical challenge of the project is to build predictive models that capture both the substance of negotiations and the dynamic patterns of human interaction. The real-life scenarios of Red&Blue introduce unique challenges, including missing or noisy modalities, shifting contexts, and diverse individual preferences. Tackling these challenges calls for advancing adaptive multimodal machine learning approaches that remain robust while engaging with practical concerns of privacy, ethics, and user agency. Throughout this project, you will contribute to data collection experiments for real-life negotiation settings.