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May 2026 Apply now Are algorithms neutral tools, or do they actively shape the world they model? In this PhD, you will bridge the gap between building and critically studying Human-Centred AI systems
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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