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/instruction tuning methods. * Understanding of Reinforcement Learning (RL) principles, multi-objective optimization, or active learning/Bayesian optimization. * Strong programming capabilities in Python
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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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Max Planck Institute of Animal Behavior, Radolfzell / Konstanz | Konstanz, Baden W rttemberg | Germany | 16 days ago
analytical skills (Frequentist and Bayesian statistical frameworks) Experience in collecting and/or analyzing observational data on animal behavior, including ‘focal and party follow’ studies, as well as ‘ad
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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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machine learning and Bayesian calibration methods to enable multi-scale, multi-physics model development. Complete simulation verification, model validation, uncertainty quantification, and documentation
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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