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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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Studies (CCSS), where you will interact with researchers working on a broad range of complex systems. Where to apply Website https://www.academictransfer.com/en/jobs/359824/phd-position-in-statistical-phy
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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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. The project is jointly supervised by Dr. Tarikere Niranjan (https://niranjangroup.weebly.com/prof-tarikere-t-niranjan.html ) and Dr. Emir Efendić (https://eefendic.com ) and examines how decision-makers in
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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