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that leverage state-of-the-art AI methods (deep learning, generative AI, Bayesian modelling, active learning, etc.) to combine cellular imaging data, chemical compound structure, viral genomes and other omics
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About the lab The Laboratory of Causal Systems Immunology combines causal inference, probabilistic AI and large-scale in vivo perturbation experiments to uncover how genes shape immune-cell states
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About the lab The Laboratory of Causal Systems Immunology combines causal inference, probabilistic AI and large-scale in vivo perturbation experiments to uncover how genes shape immune-cell states
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algorithms capable of operating in large, partially observed spatial domains to infer efficient, interpretable strategies for estimating archaeological potential, that capture distinct criteria including
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layers. Within this environment, we will design and evaluate new reinforcement learning algorithms capable of operating in large, partially observed spatial domains to infer efficient, interpretable
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, both with respect to drug resistance inference and transmission inference. You will work in a multidisciplinary research environment at the intersection of genomics, bioinformatics, epidemiology
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analysis, time-series modelling, clinical prediction, risk modelling, survival analysis, causal inference, spatio-temporal modelling, or multimodal data integration. The candidate should be motivated by
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at international conferences is highly valued; participation in patent writing is a plus; proven track record of teamwork, demonstrated through participation in joint papers or consortium-based projects. We
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fields. Strong skills in the analysis of time-series data, statistical skills, effective communication, and organizational abilities are essential. A strong track record as evidenced by international
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PhD degree in Geography, Geomatics, computer science or related disciplines. You should have a strong background and publication track record in GeoAI or GIScience. Good English communication skills