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the DFG-funded research project ICEBAY, which focuses on Bayesian hierarchical modeling and probabilistic inference for temperature reconstruction by combining borehole thermometry and ice-core data. The
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Bayesian optimization and other active learning techniques to guide experimental efforts by identifying optimal chemical compositions and processing conditions of membranes that maximize both selectivity and
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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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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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. 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 neural networks. Excellent analytical, technical, and problem-solving skills Excellent programming skills in Python and PyTorch including fundamental software engineering principles and machine