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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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to AIDS is highly variable among individuals, developing an average of 9-10 years after infection in most adults in the absence of antiretroviral therapy. The overall objective of the PhD project is to
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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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societal objectives. The program recognises the growing interconnectedness of sustainability, sustainable finance, and AI, as FsF are increasingly required to redirect capital towards sustainable investments
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decoupling the key thermoelectric characteristics using a “nanoparticle-in-alloy” design strategy within an inorganic thin-film system. The core objective of the research is to tailor the interface structure
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, a cutting-edge research initiative in forest ecology, biodiversity monitoring, and molecular methods. The project explores how airborne environmental DNA (eDNA) can be used to detect and monitor