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experimentation Bayesian optimization and active learning contextual bandits, reinforcement learning and online experimentation causal inference and treatment-effect heterogeneity statistical learning and machine
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experimentation Bayesian optimization and active learning contextual bandits, reinforcement learning and online experimentation causal inference and treatment-effect heterogeneity statistical learning and machine
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related field A strong background in machine learning, computer vision, or 3D point cloud processing, as well as in probabilistic modelling and Bayesian inference Very good programming skills, preferably in
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stochastic processes, Markov models, dynamical systems, quantum walks, or related mathematical approaches. Experience with computational model fitting, Bayesian inference, simulation, or formal model
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statistics, or decision science, who specializes in one of the following areas: statistical learning theory, causal inference, Bayesian statistical modeling, or AI and data management. [Desirable
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mathematical background Core skills: Probability and statistics. Estimation, Bayesian inference, uncertainty quantification and calibration (proper scoring rules, reliability diagrams, ECE), experiment design
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, Bayesian analysis, modern causal inference, biomarkers analyses, statistical genetics and genomics, computational statistics, systematic review methodology, structural equations modeling, and muti-omics
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, Bayesian analysis, modern causal inference, biomarkers analyses, statistical genetics and genomics, computational statistics, systematic review methodology, structural equations modeling, and muti-omics
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data and 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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cancer cohorts. • Proven ability to develop novel computational and AI-driven methodologies, including Bayesian generative models, phylogenetic inference tools, and algorithms for multimodal data