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Field
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-dependent changes in electrical impedance with real-time high-speed distance measurements. In a parallel project, another Postdoc will, partly based on two current ongoing PhD projects, design and test
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: 12 September 2025 Apply now Are you a data scientist interested in designing and implementing process-informed machine learning and uncertainties quantification methods? Join us as a postdoc and work
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approaches (incl. 16S rRNA gene metabarcoding, bioinformatics) to assess how seagrass may improve the health of low trophic aquaculture (LTA) species (e.g. mussels, oysters) under future climate scenarios
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surgery. Ideal candidates will have: A Master's degree in artificial intelligence, bioinformatics, computer science, physics, mathematics, or a similar field. Proficiency in deep learning and exceptional
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intersection of AI, imaging technology, and oncological surgery. Ideal candidates will have: A Master's degree in artificial intelligence, bioinformatics, computer science, physics, mathematics, or a similar
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and processes) by shadowing as well as receiving coaching and tutoring from one of our Senior PA Engineers. You will perform this task in parallel to specific Mission Classification activities described
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disciplines, including computer science, bioinformatics, and medicine, as part of a large European consortium. In particular, you are expected to collaborate closely with another postdoc hired at Leiden
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Processes’ and ‘Physiology and Systems Biology’. Qualifications Who The ideal candidates must be highly motivated, hard-working, ambitious, creative, enthusiastic about RNA and interested in conducting both
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during the first five years in the Netherlands. Selection process For more information about the position, please contact Prof. dr Liesje Mommer, via +31 6 20921336 or email: Liesje.Mommer@wur.nl
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passionate about linking atomistic processes to device performance through computer simulations? Are you fascinated by designing next-generation semiconductors with quantum-mechanical methods enhanced by AI