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learning algorithms. Personalizing user interactions by building models that adapt explanations to specific knowledge levels and interests of users, so that user modelling and formal reasoning transform
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reinforcement learning Enhancing transparency and contestability of decision-making processes, taking a multimodal approach to reveal the reasoning behind complex AI-driven planning and learning algorithms
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statistical physics, applied probability, and population genetics; develop inference frameworks that link model predictions to genomic and epidemiological data; design controlled computational experiments
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: formulate and analyze stochastic models of evolving populations using methods from statistical physics, applied probability, and population genetics; develop inference frameworks that link model predictions
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of clinical services to horses and companion animals, and both clinical and more fundamental research in the areas of regenerative medicine, locomotion, genetics and reproduction. More information For more
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the Reproductive Genetics team. Your job Your main tasks will consist of supporting a vibrant clinical programme in all activities related to in vitro production and preservation of horse embryos (oocyte recovery
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The project aims to identify the behavioural, neuroendocrine, and (epi)genetic mechanisms of parochialism—how cooperation and conflict emerge within and between primate groups. You will experimentally
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within the Reproduction Team of Utrecht University’s Equine Clinic and conduct research within the Reproductive Genetics team. Your job Your main tasks will consist of supporting a vibrant clinical
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degree in plant biology, bioinformatics, biochemistry, genetics or a related field. Strong bioinformatics skills, preferably in transcriptomic analyses and network inference. Expertise in molecular biology
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sciences. A selection of ongoing research lines includes the evolution of genomes, protein complexes, systems genetics, metagenomics, host-microbe interactions, and the basic principles of ecology and