Postdoctoral Fellowship (13) at Delft University of Technology (TU Delft), Netherlands

Emerging automation technologies are increasingly mediating social encounters and challenging established understandings of recognitional justice. Examples range from how users come to recognize AI-generated avatars as genuine companions, to how the labor of data annotators in AI supply chains is (or is not) duly acknowledged through digital interfaces.

In recent decades, recognitive processes have garnered growing attention in social and political philosophy, sparking renewed interest in G.W.F. Hegel’s work and its ethical dimensions. During the same period, the philosophy of technology has matured into a distinct field, with a particular focus on how concrete technological artifacts mediate human-world relations. These two strands of thought are increasingly being bridged in contemporary studies that explore recognition in the context of technological mediation, especially through AI. Yet, the question remains open as to whether—and how—mutual recognition between humans can be effectively mediated by automated technologies.

This postdoctoral position is dedicated to exploring the role of design in enabling technology-mediated recognition as a condition for human freedom. The research will contribute to an ongoing effort to reinterpret Hegel’s reflections on recognition, focusing on the idea that form-giving labor can materialize, in technical objects, the relative freedom of a given social relation, and ultimately serves as a ground for human emancipation.

The successful candidate is expected to further develop this articulation of design, recognition, and freedom by contributing original perspectives informed by their own background. Relevant areas of expertise may include Hegel’s philosophies of right and aesthetics, Marxian political economy, critical theory, existentialism, postphenomenology, or other approaches. Candidates are also expected to empirically connect their investigations to contemporary cases regarding the development of artificial intelligence and related technologies, and to contribute to scholarly debates in the field of design.

Energy storage is an indispensable factor for bridging the electricity consumption with the intermittent renewable energy supply. Electricity grids face significant challenges in balancing economic growth with sustainability targets. Rapid electrification across industrial, transport, commercial, and residential sectors strains existing grid infrastructure, leading to congestion and transmission bottlenecks. These issues limit the integration of renewable energy. Long-duration energy storage (LDES), that provide 8+ hours of storage, can balance intermittent production and demand, thereby stabilizing the grid, reducing reliance on fossil fuels, and ensuring more effective use of renewable energy. TenneT expects 4.9 GW of large-scale battery storage is required in The Netherlands by 2030, while there was only 0.14 GW installed in 2023. Hence, a rapid scale-up of LDES is needed.

In that light, we started a collaborative project, with universities (TU Delft, UTwente, TU/Eindhoven, HAN) and companies (AQUABATTERY, Elestor, Exergy Storage, RWE, Nobian), to develop batteries for long-duration energy storage. Specifically, this postdoc position focuses on acid-base flow batteries, and more specifically multiphysics simulations of the liquid and ions in acid-base flow batteries. Acid-base flow batteries are based on dissociating water into acid and base during charging, using bipolar membranes, and recombining acid and base in this membrane to retrieve electrical energy.

We’re looking for an excellent postdoc, with strong simulation skills, to investigate how flow geometries, membrane properties and operational conditions can improve the technology of acid-base flow batteries. The aim is to better understand transport of ions through the (bipolar) membranes and the associated non-idealities (such as ion crossover, concentration polarization and non-uniform current distribution) and develop strategies for enhancing the energy efficiency. Close collaboration with particularly the company AQUABATTERY will be required, and you will work together with a group of postdocs and other researchers on developing components (membranes, electrodes, spacers/flow fields).

We’re looking for an excellent postdoc, with strong engineering skills, to study stack designs and operational conditions, experimentally, to increase the power density, energy density and stack lifetime. The aim is to better understand the effects of each design aspect and to develop new strategies to improve the technology of acid-base flow batteries. This includes developing designs for flow fields and spacers, assessing different electrolytes, studying sealing properties and interaction with the (bipolar) membranes, and developing sensors for fast analysis of the stack performance. The work could also include advanced characterization of flow distribution (such as optical dye experiments and MRI flow experiments) and strategies for new operational modes that affect the battery performance. Close collaboration with particularly the company AQUABATTERY will be required, and you will work together with a group of postdocs and other researchers that develop components (such as membranes and electrodes).

For this postdoc position, you need to have an outstanding scientific track record, good skills for engineering and experience in electrochemical systems. Your daily operation is in the EFS research group of David Vermaas. The work of our group addresses electrochemical flow systems, including applications of electrolysis, water technology, CO2 capture and flow batteries. You will collaborate with other researchers in the department and with companies in this project. The work will also contribute to TU Delft’s e-Refinery institute on electrochemical conversion that includes >20 principal investigators across the campus, where electrochemical advances are used and valorised in upscaled prototypes, in collaboration with industrial partners. 

We are seeking a highly motivated Postdoctoral Researcher who will be working on two closely related research projects addressing key subsurface challenges in geothermal energy production and CO₂ storage. The role focuses on the development, implementation, and benchmarking of advanced numerical models to better understand and manage induced seismicity, fault reactivation, and near-wellbore processes such as salt precipitation and hydrate formation. The work integrates processes across scales, from near-wellbore physics to reservoir- and fault-scale behaviour, and supports the development of robust, physics-based modelling tools for subsurface energy applications. Main repsonsibilities include:

• Design and implement benchmark scenarios for (shear) stress modelling along faults and seismic modelling in geothermal operations, supporting fault stressing and seismicity analyses.
• Establish validated reference scenarios for this benchmark with varying levels of complexity, including cases with analytical or semi-analytical ground truths.
• Develop accurate near-wellbore physical models for salt precipitation and hydrate formation during CO₂ injection, explicitly accounting for impurities in the CO₂ stream.
• Bridge near-wellbore, reservoir, and fault-scale processes through consistent numerical modelling and upscaling.
• Support model evaluation, comparison, and performance assessment across both projects, in close collaboration with internal and external stakeholders.

Geothermal energy is a local and essential source for heating in the energy mix of the future and must be sustainably scaled up. However, seismicity is a major societal concern. Currently, conservative assessments of seismicity (i.e., predicting higher seismicity than likely to occur) are one reason for projects not passing the exploration stage, and seismic activity related to gas extraction has resulted in a public acceptance risk of all subsurface activities. In this project, we are going to address the following scientific question: ‘When can reservoir cooling in sedimentary geothermal projects lead to significant seismic activity?’

We are seeking a highly motivated Postdoctoral Researcher to lead the development of advanced numerical models to simulate thermo-hydro-mechanical (THM) processes in sedimentary geothermal reservoirs, with a specific focus on thermally induced stress changes, fault reactivation, and induced seismicity. The work will extend the open-source DARTS simulation framework to include:

• Thermo-mechanical coupling with realistic fault representations
• Advanced constitutive models capturing contractive and dilative fault behaviour
• Stochastic representations of heterogeneous reservoir and fault properties
• Efficient high-performance computing implementations, including GPU acceleration

The developed methods will be applied to realistic case studies, including the TU Delft campus geothermal project, and will quantitatively compare results against existing seismic risk assessment approaches.

Key responsibilities include, but are not limited to:

• Develop and implement numerical methods for coupled THM processes in geothermal systems
• Extend constitutive and fault-slip models within the DARTS framework
• Perform large-scale stochastic simulations to quantify uncertainty and seismic risk
• Analyse the temporal evolution of stresses, strains, and energy release
• Collaborate closely with experimental and monitoring researchers, and external collaborators such as TNO, to integrate laboratory and field data
• Publish results in high-impact, peer-reviewed journals and contribute to open-source software

Photosynthetic microalgae hold promise for the sustainable production of high-value products, bioplastics, biofuels, and future engineering living materials. Flowing suspensions of living microalgae cells represent a new kind of living fluid that physiologically responds to the environment and flow conditions. New insight into the flow dynamics of these living microalgae suspensions is crucial to model algae blooms or develop new flow technologies for bioreactors.

The postdoc position is available within the project “Flow4Algae”. This experimental project aims to understand the multiscale fluid dynamics of living microalgae suspensions. We are particularly interested in the interplay between flow conditions, cell physiology, and cell growth. Each different project will focus on flow conditions ranging from linear shear flows in microfluidics to weak turbulence. In this project, you will design new multiscale experiments and analytical tools for the different flow regimes. These studies will be conducted in the BioFluids Laboratory in TU Delft and will use the infrastructure in the laboratory, including advanced flow diagnostics (Particle Image Velocimetry, Particle Tracking Velocimetry, and Laser Induced Fluorescence), microscopy (3D tracking multi-view microscopy and Fluorescence), rheology tools, microfluidics, and existing flow dynamics setups.

As a postdoc, you will be part of a vibrant team of researchers working on the project Flow4Algae, with diverse backgrounds and expertise. The group is part of the Process & Energy Department, which thrives to conduct world-class research & education focusing on process & energy technologies for sustainable development.

Thermoplastic composites are an important class of engineering materials used in applications ranging from protective covers and frames to structural load-bearing components in automotive and aerospace. Their excellent strength-to-weight and stiffness-to-weight ratios, in combination with processability into complex geometries, makes them a key engineering material in applications where weight is an important design factor. Scaling up the manufacturing of thermoplastic composites in a viable and sustainable way, however, poses a major challenge. While the manufacturing of individual parts is at a proper level of maturity, the integration and assembly of these parts is far less developed. “Enlighten” is large multidisciplinary research program funded by the Dutch Research Council (NWO) on enabling integrated lightweight structures with thermoplastic composites involving several academic and industrial partners.

This postdoctoral research project within the Enlighten program aims to validate the capability of a newly developed and implemented modelling framework to describe the fracture and fatigue of a T-section taken from an overmolded thermoplastic composite part. The framework combines a cohesive zone model for the overmolded interface with an anisotropic viscoplasticity model for the laminate, specifically developed to enable prediction of the long-term performance of composites under high-cycle fatigue conditions. The researcher will be responsible for an experimental campaign on T-shaped, overmolded structures loaded under both quasi-static and cyclic boundary conditions, in part by supervising thesis students on. Moreover, simulations will be performed to validate the frameworks capability of describing failure of overmolded interfaces under both short- and long-term loading after calibrating the model on a subset of the experiments.

The project will fill a gap in literature regarding the characterization of overmolded thermoplastic composites and will investigate the validity of a leading computational model for describing the long-term performance of such integrated composite structures. The research will be supervised by Dr.ir. Tom Engels at Eindhoven University of Technology, where experiments will be performed, and Dr.ir. Frans van der Meer at Delft University of Technology, where the model development has taken place.      

GSP is still in its infancy and we are yet to discover its full potential. This project takes a crucial step in this direction. Current GSP tools mostly consider the support of the signal (i.e., the graph) to be time-invariant or static. This is true when the data can be assumed stationary and the dimensions of the data do not change. The main innovation GraSPA introduces is to extend GSP tools to dynamic graphs, where we distinguish between two types of changes: changes in the connections (CC) due to for instance nonstationary data, and changes in the number of nodes (CN). The CC scenario occurs for instance in brain activity monitoring where the connections between different brain regions change over time depending on the performed task. The CN scenario is of importance in recommender systems where new users/items enter the system. For these two types of dynamic graphs, we propose contributions in two key directions in GSP. First, we will extend existing tools for graph topology identification to dynamic graphs. Such approaches will be data-driven and aim at revealing the hidden structure in large amounts of data (e.g., coming from brain activity monitoring and recommender systems). For the CC scenario it will be crucial to track how the connections between nodes change over time, whereas for the CN scenario we want to learn a model for the connections of the new nodes entering the graph. And second, we will propose graph filtering approaches with limited storage, data transfer, and computational needs to process the data (remove noise and outliers, highlight specific graph frequencies, or zoom in on certain localized features) as well as to predict missing or future values (e.g., ratings in a recommender system or future brain activity). 

Offshore wind energy is a cornerstone of Europe’s transition to a carbon-neutral energy system. As offshore wind capacity in the Netherlands rapidly expands and existing assets age, there is an urgent need for cost-effective and sustainable maintenance strategies. Wind turbine blades are among the most failure-prone components due to their composite construction, large size, and exposure to harsh cyclic loading. Current inspection-based maintenance approaches often lead to conservative lifetime estimates, premature blade replacement, and significant economic and material losses. 

This postdoctoral position is essential to enabling predictive maintenance and circular end-of-life strategies for offshore wind turbine blades. The role focuses on developing advanced prognostic models that can reliably predict the Remaining Useful Life (RUL) of wind turbine blades’ components. By transforming diagnostic inspection data into actionable predictions, the work will directly contribute to reducing downtime, lowering operational costs, and improving the sustainability of offshore wind operations.

You will develop, implement, and validate prognostic models for composite blade structures, with a focus on integrating diagnostic information from advanced Digital X-ray inspections into physics-informed prognostic frameworks. Key responsibilities include uncertainty quantification and management in RUL predictions, model validation using experimental data, and contributing to scientific publications and project deliverables. 

Warm tropical waters fuel intense storms, yet the fine scale exchanges of heat, momentum and freshwater between air and water remain poorly understood. These interactions shape local weather extremes and climate variability, but current models miss them. QUASI turns Lake Victoria—Earth’s largest tropical lake—into an open air laboratory to uncover how sub kilometer air–lake coupling influences convection, rainfall and heat budgets. The insights will advance coupled models and improve forecasts in vulnerable regions. As a postdoctoral researcher, you will play a pivotal role in collecting, curating and analysing this unique dataset, enabling insights that will transform high resolution climate modelling and support regional forecasting capacity.

Understanding how the brain works is one of the most profound scientific challenges of our time. To that end, the development of new imaging methods is one of the most powerful ways of transforming a research discipline. In the context of a grant of the Human Frontier Science Program (HFSP), the Maresca Lab at TU Delft is looking for a postdoctoral candidate to advance ultrasound imaging of the human brain. Specifically, the task will consist in developing fully noninvasive volumetric ultrasound neuroimaging augmented with skull aberration correction. The long term goal of this interdisciplinary project is to study the neural basis of language development. The candidate will be hosted at the Maresca Lab, which is part of Imaging Physics department of TU Delft. The lab currently consists of 10 members with interdisciplanary skills. The candidate will also coordinate with two postdocs and two principal investigators located in Japan and in the United States.