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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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researcher to develop innovative quantitative weight-of-evidence approaches based on Bayesian networks for environmental burden of disease studies. The project aims to establish a novel framework for
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must be inferred from models that are incomplete and data that involve errors. For such challenges, Bayesian analysis using Markov Chain Monte Carlo (MCMC) has become the gold standard. For addressing
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), and ELLIS Institute Finland. You will be supervised by Dr Julien Martinelli and Associate Professor Harri Lähdesmäki. The position offers a broad local research network in Bayesian machine learning and
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Associate Professor Harri Lähdesmäki. The position offers a broad local research network in Bayesian machine learning and computational biology. Your network and team Dr Martinelli is an independent
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directionality and causal inference. This project instead builds on the structure of the central dogma, using genetic variation as an anchor for causal inference across molecular layers (genomics
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of deep learning will investigate questions such as the structure and expressivity of emerging neural architectures relevant to space, such as implicit neural fields, continuous normalising flows