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Field
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. This robust combination drives substantial advancements in optimization, sampling, inference, and machine learning. On one side, statistical approaches such as Bayesian inference play a critical role in
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experience in one or more of: large-scale data analysis, time-series photometry, spectroscopy, astrometry, Bayesian/statistical inference, and/or software development for astronomical datasets. Department
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physics, Bayesian inference, and complex systems theory. You will contribute to method development, simulation and validation in close collaboration with experimental partners. Key Responsibilities: Carry
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experience in AI teaching We understand AI very broadly – and adequate experience would include most topics in modern statistics and topics like Bayesian Machine Learning and Simulation Based Inference (a past
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and conducting climate model simulations and analysing large volumes of ESM simulations. Knowledge of reduced-order modelling and Bayesian inference is highly valued, and experience with climate
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Bayes factor hypothesis tests in factorial designs. What are you going to do The envisioned projects will focus on the following activities related to Bayesian inference in factorial designs: Construction
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Details Panel (longitudinal) data enables learning the dynamics and relations of (groups of) units, strengthening the inference on both cross-sectional and dynamic parameters. The dominant approach
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experience would include most topics in modern statistics and topics like Bayesian Machine Learning and Simulation Based Inference (a past research focus on neural network architectures is not a prerequisite
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Bayesian Index Tracking: optimisation by sampling School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr Kostas Triantafyllopoulos, Dr Dimitrios Roxanas Application
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, probabilistic programming, Bayesian deep learning, causal inference, reinforcement learning, graph neural networks, and geometric deep learning. It comprises Jan-Willem van de Meent, who serves as director, Max