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University of Oslo and UiT The Arctic University of Norway | Norway, Michigan | United States | 38 minutes ago
Bayesian inference and foundation models Employment: University of Oslo, Department of Mathematics PhD programme: University of Oslo, Faculty of Mathematics and Natural Sciences Starting date: not
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across molecular, cellular, and tissue scales. Research areas of interest include biological imaging and spectroscopy; statistical and Bayesian inference; machine learning for biological discovery
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directionality and causal inference. This project instead builds on the structure of the central dogma, using genetic variation as an anchor for causal inference across molecular layers (genomics
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collegial, research-active department with strengths in Bayesian methods, high-dimensional statistics, computational statistics, physics-informed inference, and biostatistics, and will help shape its
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Ability to program in Python and develop automated simulation, data-processing, and inverse-identification workflows Familiarity with optimization, Bayesian inference, uncertainty quantification, or
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optimization, Bayesian inference, uncertainty quantification, or machine learning methods applied to engineering problems Ability to analyze and interpret experimental and simulation data, prepare peer
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data integration, especially in molecular medicine; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal
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sequential experimentation Bayesian optimization and active learning contextual bandits, reinforcement learning and online experimentation causal inference and treatment-effect heterogeneity statistical
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sequential experimentation Bayesian optimization and active learning contextual bandits, reinforcement learning and online experimentation causal inference and treatment-effect heterogeneity
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trial design, and sequential experimentation Bayesian optimization and active learning contextual bandits, reinforcement learning and online experimentation causal inference and treatment-effect