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” experimentalists in the research group and with interdisciplinary collaborating scientists. Elements of the experimental approach will include: Bayesian reconstruction of events on billion-year timescales
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, active learning, Bayesian optimization, agentic AI, or closed-loop materials discovery. Experience in computational heterogeneous catalysis, electrocatalysis, surface science, electronic-structure analysis
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/ Deep Learning Knowledge of: Active learning, Bayesian optimization Reinforcement learning or decision-making systems Experience with: Python ecosystem (PyTorch, Scikit-learn) Data pipelines and
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of multi-modal healthcare record data. The ideal candidate will additionally have experience: Multi-modal AI model development Statistical modelling techniques (Bayesian inference, differential equations and
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the production of the CBC catalog. The successful candidate is expected to have strong analytical skills and experience with signal processing, Bayesian statistics and machine learning. Exemplary
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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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motivated researcher to develop a strong independent research profile at the interface of Bayesian statistics, clinical trial design, optimization, computational statistics, and/or translational cancer
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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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monitoring Preferred Qualifications: Experience working with any of the following: Bayesian hierarchical modeling, occupancy modeling, joint species distribution models, integration of multiple data types
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understanding of data quality, reproducibility and robust analytical practice. Experience of SQL, cloud-based or high-performance computing environments, and Bayesian methods would also be valuable. Beyond