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medical applications. Federated Bayesian learning offers a solution to those problems by allowing multiple participants to train machine learning models collaboratively, without sharing any data. Bayesian
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Skłodowska-Curie (MSCA) funded European doctoral network that unites leading institutions to train a new generation of researchers in health economics, causal inference, and the analysis of large-scale
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insights that inform biodiversity management. The project includes: · Apply of deep learning models to annotate bird and bat species from sound recordings. · Develop a Bayesian statistical
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Skłodowska-Curie (MSCA) funded European doctoral network that unites leading institutions to train a new generation of researchers in health economics, causal inference, and the analysis of large-scale
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include developing new automated program verification techniques specifically for such programs, as well as specification inference and fault localisation approaches that would make such verifiers practical
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that yield valid statistical conclusions (inference) on causal effects when using machine learning algorithms and big datasets. The project is part of the research environment Stat4Reg (www.stat4reg.se ), and
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to conduct one's own postgraduate education. This includes, in addition to completing mandatory doctoral courses, using sequence analysis (including phylogenetics [both maximum-likelihood and Bayesian analysis