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optimization-based updates (e.g., stochastic gradient methods and Bayesian learning), Probabilistic performance guarantees, leveraging tools from stochastic systems, RKHS-based learning, and Bayesian inference
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. mixed effects regression models and/or Bayesian statistics; e.g., brms / lme4 packages). Excellent written and spoken English. Desirables (traits that would give you an advantage) Training in evolutionary
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embedded in the CCSS community. Nice to have Experience with viral genomics, phylogenetics, Bayesian or likelihood-based inference, infectious disease modeling, or high-performance computing. Additional
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Experience with viral genomics, phylogenetics, Bayesian or likelihood-based inference, infectious disease modeling, or high-performance computing. Our offer a position for 18 months, with an extension to a
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programming, Bayesian deep learning, causal inference, reinforcement learning, graph neural networks, and geometric deep learning. In particular, you will be part of the Causality team under the supervision
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work closely with the other PhD candidate of PAST, who creates high-resolution proxy-based reconstructions of the same paleoclimate. Together, you apply a Bayesian statistical framework to contrast and
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. Desirable Familiarity with supply chain management, operations, or organizational contexts. Experience with advanced statistical methods (e.g. multilevel modelling, causal inference, Bayesian methods
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, machine learning, deep (reinforcement) learning, Bayesian optimisation, control theory, dynamical system theory and/or robotics. Experience with hardware development is desirable but not mandatory. You have