06 Postdoctoral Fellowship at Eindhoven University of Technology, Netherlands 

This project aims to understand how the C2CA recycling process can effectively upcycle concrete with various impurities, such as bricks and ceramics. By investigating these materials, we seek to determine how to produce high-quality SCMs. Firstly, methods will be developed to improve the properties of recycled concrete, ensuring strong and durable end products regardless of their origin. Secondly, this project will explore blends of RCFs with other mineral residues, such as dredged sediments or excavation waste, to create new, high-performance SCMs. Thirdly, lab-scale tests will be conducted to evaluate the performance and durability of the new SCMs, ensuring their reliability for construction applications.

The postdoc researcher will join the Building Materials Group which contains 25 PhDs, 3 postdocs, 2 assistant professors, 1 associate professor, and 1.4 full professors. This research group works on new, sustainable, and circular building materials containing or synthesized from secondary raw materials with good chemical, physical and aesthetic properties, and added functionalities. Examples are natural fibers, fly ashes, nano-silica, aerogels, slags or recycled aggregates. Functionalities, including fire resistance, air purification, self-cleaning, high performance, and hydrophobicity, are investigated and developed for these building materials. Moreover, durability and sustainability, such as the degradation of, or leaching from, building materials when exposed to aggressive service environments, are studied. The investigations are carried out based on experimental observations and modeling.

As Post-doctoral Researcher within VICI Project “Geometric Learning for Image Analysis” your main tasks are:

• Carry out research in the research group Geometric Learning and Differential Geometry within the Centre for Analysis, Scientific Computing and Applications (CASA) of the Department of Mathematics and Computer Science at TUe, supervised by principal investigator Remco Duits,
• Publish research results in renowned journals and conferences, focusing on:
    A) Equivariant, PDE-based image processing and machine learning for industrial image analysis (in thesemiconductor industry),
    B) Automatic geometric vessel tree tracking and analysis in medical image analysis applications,
• Assist in the supervision of PhD and MSc students and collaborate with (health) tech partners,
• Report on the results in project deliverables, papers and conference contributions.

Recently, high entropy alloys (HEAs) have emerged in materials science, due to their vast compositional space, which provides enormous tunability of physical and chemical properties. HEAs are of particular interest to (electro)catalysis, since it enables to reduce the proportion of noble metals, without compromising performance. Recent reports have shown the potential of HEAs for electrocatalytic water splitting. Despite promising performances, reports on the application of HEAs in oxygen evolution reactions are still scarce, and the catalytic mechanisms of HEA electrocatalysts are poorly understood.

In this collaborative postdoc project between TU Eindhoven (TU/e) and Utrecht University (UU), you will synthesize new HEA materials (at UU), and assess their electrocatalytic performance in the oxygen evolution reaction (at TU/e). The overall aim is to unravel the relation between chemical composition and electrocatalytic performance, using a wide variety of analysis techniques including X-Ray diffraction, XPS, SEM-EDX, together with in-situ Raman and IR during electrocatalytic experiments. Finally, stability measurements will be done in collaboration with a chemical company.

As a postdoc at the Technology, Innovation Society Group at TU/e, you will develop and apply methods to fairly assess the environmental impacts of circular (and integrated) PV applications. The postdoc position is part of the Interreg project PV RESILIENCE. The aim of this project is to develop new technologies for locally produced, and circular solar electricity products while taking into account the needs of user, society and a circular economy.

Life cycle assessment is a widely accepted tool to assess and compare the environmental impacts of products and prospective LCA for emerging technologies is gaining traction at least in the academic world. However, methods are still lacking to suitably assess the environmental profile of circular PV, as current approaches rely on state of the art processes and assumptions or ignore end of life and secondary life cycles in general. Current methods also ignore quality demand of the materials and future alternatives or applications. In this job, you will combine prospective LCA with material flow modelling to develop scenarios and methods that can better guide technology development. These methods will be applied to the innovations developed by partners within the PV RESILIENCE projects. You will work closely together with the technology developers in companies and knowledge institutes the project to support their development process while also publishing in scientific journals on new methodological insights.

The Technology, Innovation, and Society group at Eindhoven University of Technology straddles natural science, social science and humanities disciplines and provides a stimulating and collaborative environment. The TIS group is part of the Department of Industrial Engineering & Innovation Sciences (IE&IS). Research at TIS concerns how humans and societies bring about technological change, and how technological innovations change society. With ~25 tenured staff and a large group of postdocs and PhD candidates, researchers in the TIS group try to bridge the engineering and social sciences in a systems perspective on sustainability transitions. The TIS staff teaches in the BSc program ‘Sustainable Innovation’, the MSc programs ‘Sustainable Energy Technologies’ and ‘Innovation Management’, and in university-wide programs for engineers.

Power electronics converters are systems that process electric power in various applications, including renewable energy generation, high-tech systems, electric vehicles, industrial automation and more. At EPE Group, we are building a strong research line on the reliability and resilience of power electronics systems. The innovation of power devices is one of the driving forces behind the advancement of power electronics systems. As typical wide-bandgap power devices, SiC MOSFETs are gaining popularity in various applications, and the technology continues to evolve. The new devices always require a strict reliability assessment before they can be deployed in mission-critical applications.

Your project focuses on the reliability of next-generation SiC MOSFETs. This research is part of a collaborative Horizon Europe initiative involving leading industrial partners and academic institutions across the EU. The focus of your work will be on reliability analysis and diagnostics of medium-voltage SiC MOSFETs, including the development of advanced test platforms for device and packaging evaluation. Key responsibilities include conducting lifetime modeling, developing advanced condition monitoring techniques, and applying data-driven analytics for lifetime prediction. You will play a central role in integrating experimental insights with modeling approaches to support robust, long-term performance of SiC devices. You will work closely with industrial and academic partners and presenting your findings and ideas to the scientific community is an essential part of your work. Additionally, you are expected to contribute to education at TU/e as part of your job responsibilities.

Today, companies across a wide range of industries face the challenge of managing increasingly complex stochastic systems, where uncertainty is inherent and data is abundant. These systems arise in diverse domains—such as manufacturing, rental platforms, and production systems—where decision-making must balance conflicting objectives, leverage real-time data, and ultimately support sustainable profitability.

Examples include optimizing production speeds to balance output, equipment deterioration, and energy consumption in manufacturing, or designing dynamic pricing and allocation policies in rental systems to match supply with fluctuating demand. Similarly, in inventory management, determining replenishment and allocation strategies under demand uncertainty is central to ensuring both efficiency and service quality.

To address these challenges, advanced methodologies and algorithms are needed to design effective revenue and inventory management strategies for complex stochastic systems. The growing availability of data and connectivity through the Internet of Things create new opportunities to incorporate real-time insights into decision-making, combining tractable modelling with provably efficient solution methods.

As a postdoctoral researcher, you will have the opportunity to conduct cutting-edge research on revenue management and/or inventory management problems within complex stochastic systems. Your work may involve formulating new models, analysing structural properties, and developing innovative algorithms with both theoretical rigor and practical relevance.