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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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of working cooperative and team-orientated working style Specific Requirements experience with research in teams knowledge of the fundamentals of Bayesian statistics knowledge of the fundamentals of radio
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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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environmental factors such as fluctuating wind speeds and saltwater exposure. Using advanced statistical and machine learning techniques, including Bayesian inference and stochastic modelling, the project will
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. Bonus lectures can be picked by the students depending on their interests and project-specific requirements. Students can deepen their knowledge about selected topics (e.g. Bayesian Statistics, HMMs, AI
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Bayesian Networks (DBNs) for probabilistic risk modelling Scenario-based simulation for rare-event analysis You will be part of a dynamic, interdisciplinary research setting at one of Europe’s leading
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statistical and machine learning techniques, including Bayesian inference and stochastic modelling, the project will quantify and analyse uncertainties in the design and operational performance
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Science, Telecommunications, Applied Mathematics, or related fields; Solid background in probabilistic modeling, Bayesian inference, information theory, and/or machine learning; Experience with signal processing or decision
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receive training and skills in some of the following: meta-barcoding, stable isotope analysis, trophic-web analysis, Bayesian statistics, wet-lab experimentation – respirometry, fieldwork. Previous
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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