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We are looking for a highly motivated postdoctoral researcher to join an interdisciplinary research project on enzymatic PFAS degradation at the Department of Biological & Chemical Engineering of
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multi-physics modelling, autonomous materials discovery, materials processing, and structural analyses. We also focus on educating engineering students at all levels, ranging from BSc, MSc, PhD
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quantification Develop and apply ML methods for transfer learning between applications Dissemination of high-quality models and data to an interdisciplinary team Publish scientific papers Assisting in data
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such as the circular economy, technology, urban, energy, and environmental planning, as well as sustainable transition and design. Sustainability is central to our work in environmental assessment and
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that are trying to establish the indispensable building blocks for the quantum technology of tomorrow. Responsibilities You overall focus will be to model single-photon emitters in 2D materials and their
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decomposed into modular sub-components that can be either process-based models and/or deep learning models. MCL has the flexibility to replace any uncertain process description with a deep learning model
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). The application deadline is March 15, 2026, at 11:59 PM/23:59 (CET/CEST) Assessment and selection process Applications will be assessed by an assessment committee. Shortlisting may be applied, and only shortlisted
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in modeling and analysis of cyber-physical systems At the Technical Faculty of IT and Design, Department of Computer Science, a full-time postdoc position in modeling and analysis of cyber-physical
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, physics, computer science, applied mathematics, or similar Required competences Strong background in image processing and analysis, especially Deformable image registration and 3D segmentation methods
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Two postdoctoral positions (3-year) in Experimental Evolution of Methanogenic Microbiomes in Bioe...
project at the interface of directed experimental evolution, microbial ecology, and bioelectrochemical engineering. The project combines laboratory evolution approaches with omics-based analysis