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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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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
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clinical and more fundamental research in the areas of regenerative medicine, locomotion, genetics and reproduction. You will be part of the Drive-RM external link research consortium, which aims to develop
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experiments on collected forest soils by studying genetic (PCR) analyses, phospholipid fatty acids (PLFA) analyses, microbial biomass, soil carbon, respiration and enzymatic activities. The project is embedded
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carry out soil sampling and tree growth measurements using dendrometers; Carry out laboratory experiments on collected forest soils by studying genetic (PCR) analyses, phospholipid fatty acids (PLFA