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. The postdoctoral scholar will contribute to an exciting research program within the Foy Lab (www.foylab.xyz/) https://foylab.xyz/ , developing machine learning, computational, and mathematical models for improving
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-throughput screening, and online/in situ characterization with active-learning and Bayesian-optimization pipelines to guide experiment selection Build agentic artificial intelligence (AI) workflows and FAIR
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interdisciplinary collaboration. Faculty and students conduct innovative research in Bayesian methods, causal inference, data science, machine learning, statistical genetics, longitudinal and survival analysis, and
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, multilevel modeling, causal inference, Bayesian methods, computational modeling, and machine-learning approaches. Responsibilities include maintaining an active program of research, pursuing external funding
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Control. Com- bine reinforcement learning, optimal control and Bayesian optimization to discover robust control proto- cols under realistic experimental imperfections; apply ex- plainable AI to extract
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statistical models (Bayesian approaches, geographical analyses) adapted to environmental data. • Carry out statistical analyses and the spatial distribution of risk between residential environmental exposures
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for scientific dissemination and publication. Contest Procedure Applications must be exclusively submitted on the admissions platform of the Instituto Superior Técnico at https://fenix.tecnico.ulisboa.pt/fenixedu
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to high-dimensional statistics; Bayesian statistics; resampling techniques; digital twins; uncertainty quantification; foundations of machine learning and artificial intelligence; optimization theory and
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to accelerate formulation discovery. Experimental data will be organised into a comprehensive database and analysed using statistical learning and Bayesian optimisation, establishing a closed-loop framework
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is available at https://colalab.ai/ . At EI, the group will develop an ambitious Generative Digital Biology (GDB) programme, developing multimodal, cross-organism and experimentally grounded AI systems