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will work with existing FH patient cohort data sets from France, Portugal, Czech Republic, and Turkey, which will be expanded with multi-level multi-omics data, including genetic, transcriptomic
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or computational Affinity for computer simulations A finished PhD thesis (at least submitted to the PhD committee by the time you start) Terms and conditions The appointment will be for 20 months (based on a
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. You’ll be part of a dynamic, collaborative environment that values innovation, rigor, and translational impact. Information and application Apply by 23:59 on August the 21th, 2025. Interviews will
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, Computer Science and Artificial Intelligence. The position is embedded in the Multi-Agent Systems group of the department of Artificial Intelligence of the Bernoulli Institute. Qualifications PhD in Computer Science
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these with quantitative and qualitative research methods to promote a regenerative food system in the Netherlands? If you have a PhD degree and a skill set that puts you in a good position to carry out
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. Qualifications PhD in Computer Science, Economics, Mathematics, or a closely related dis-cipline. Strong background in computational social choice, algorithmic game theory, or AI safety. Excellent research track
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PhD degree in Chemistry, Material Science or related filed. Solid knowledge and demonstrated skills in polymers chemistry are essential; expertise in physical organic chemistry, coatings and formulation
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dynamical systems. The position is part of the research project “A Rigorous Framework for Transient Random Dynamics”, funded by the Dutch Research Council (NWO). You will be part of a team with a PhD student
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exoskeletons and bionic limbs. You’ll be part of a dynamic, collaborative environment that values innovation, rigor, and translational impact. Information and application Apply by 23:59 on August the 24th, 2025
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sustainable way of dealing with their health conditions. The technical challenge within this project lies in the huge variance of the data. Furthermore, the app needs to be able to work with sparse incomplete