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of surface sites makes theoretical understanding difficult. This project will develop and benchmark machine learning models to predict local electronic density of states (DOS) at alloy catalytic sites
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Areas: Theoretical Physics / Statistical physics Computational Science and Engineering / AI/ Machine Learning , Artificial Intelligence , Data Sciences , Machine Learning Machine Learning Complex
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qualifications You have graduated at Master’s level in computer science, computer engineering, human-computer Interaction, media technology, visual learning and communication, or closely related fields
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allocation proposals, conducting machine learning workflows, and developing complete models. Example applications include microscopy image data, cryo-electron microscopy, structural prediction and dynamic
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design, and/or machine learning in the context of integrated photonics. We are looking for someone who wishes to work theoretically in this field, while still maintaining close contact with experiments
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groups working towards a common goal. For this postdoc project, we seek a dynamic and motivated candidate with an interest in computational electromagnetism, inverse design, and/or machine learning in
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Research Infrastructure? No Offer Description This position offers a unique opportunity to work at the intersection of statistical machine learning, control theory, and transport safety, in collaboration
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This position offers a unique opportunity to work at the intersection of statistical machine learning, control theory, and transport safety, in collaboration with researchers at Chalmers and the
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, and use of these relations to infer new knowledge (i.e. reasoning); (ii) explore object affordances, learn the consequences of the actions carried out and enrich the knowledge base (i.e. learning by
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is on analysing first-person descriptions of conscious experiences with the help of machine learning and large language models (LLMs) to identify, compare, and systematize different types of states of