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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 | 11 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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, Z. F. Confidence as Bayesian Probability: From Neural Origins to Behavior. Neuron 88, 78–92 (2015). 3. Foucault, C. & Meyniel, F. Two Determinants of Dynamic Adaptive Learning for Magnitudes and
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Chekouo and his collaborators within and outside the University of Minnesota. The research will focus on the development of Bayesian statistical/machine learning methods for the data integration analysis
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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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. Experience with uncertainty quantification, Bayesian inference, inverse modelling, parameter estimation, or model calibration. Experience with high-performance computing, surrogate modelling, reduced-order
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carbon, nitrogen, and water flows in agroecosystems. A solid background in uncertainty quantification, applied statistics, Bayesian calibration, and Monte Carlo simulations. Strong skills in scientific
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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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areas will be considered when selecting candidates: Machine Learning, Neural Networks, Numerical solutions of Partial Differential Equations and Stochastic Differential Equations, Numerical Optimization
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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