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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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is part of the MET2ADAPT Doctoral Network (Meta-Materials and Meta-Structures for Adaptable, Resilient and Sustainable Renewable Energy Power Plants), a prestigious Marie Skłodowska-Curie Doctoral
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restoration ecology (see https://www.slu.se/en/about-slu/organisation/departments/department-of-wildlife-fish-and-environmental-studies/ ). The department has many international employees and well-established
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funded PhD position on Uncertainty Quantification and Technology Qualification for Advanced Wind Turbine Components. This position is part of the MET2ADAPT Doctoral Network (Meta-Materials and Meta
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to make decisions for localization, navigation, and cooperation. Within the ERC Starting Grant project CUE-GO – Contextual Radio Cues for Enhancing Decision-Making in Networks of Autonomous Agents
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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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Bayesian neural networks. Excellent analytical, technical, and problem-solving skills Excellent programming skills in Python and PyTorch including fundamental software engineering principles and machine