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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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-informed / simulation-aware modeling Efficient algorithms for design-space exploration (e.g., surrogate modeling, Bayesian optimization, differentiable programming) Hybrid approaches combining data-driven
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Your Job: This research primarily seeks to incorporate advanced neuron models, such as those capturing dendritic computation and probabilistic Bayesian network behavior, into unconventional
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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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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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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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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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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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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