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, contribute to a better world. We look forward to receiving your application! We invite applications for a fully funded PhD student position to join the research group of Jan Glaubitz to work on Bayesian
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. They have led to a plethora of important downstream applications, such as image and material generation, scientific computing, and Bayesian inverse problems. At the core of these models are differential
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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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public health. Is proficient in modern statistical modelling, AI & machine learning methods (e.g. system identification, regression models, Bayesian methods, deep learning). Is an experienced programmer in
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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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. Is proficient in modern statistical modelling, AI & machine learning methods (e.g. system identification, regression models, Bayesian methods, deep learning). Is an experienced programmer in R and/or
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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