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information at: http://www.ifw-dresden.de . The Institute for Metallic Materials (Prof. K. Nielsch) of the IFW Dresden offers Postdoc position (m/f/d) on the following topic: Thermoelectric Energy Harvesters
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scientific journals and conferences Your Profile: A doctoral degree in Physics, Materials Science, Computer Science, Data Science, or related fields Proven experience with large language models (LLMs), natural
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Postdoctoral Researcher in Citizen Science & Societal Engagement for an Inclusive Energy Transition,
position is part of the NWO-funded EmPowerEd project , a large consortium led by the TU Eindhoven . The aim of this project is to "Enable Positive Energy Districts through citizen-centred socio-technical
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to evaluate to what extent ongoing scientific discrepancies and uncertainties are a consequence of (i) people using different methodological approaches, (ii) the types of data considered (including possible
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and molecular mechanisms that act within these sites, and to determine how these events lead to a balanced immune response in different conditions and physical constrains imposed by each organ
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gathered from sites in five different languages – English, Spanish, Italian, Korean and Indonesian. This analysis can provide a wealth of information about the characters in a story, the genre, what a story
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combine large-scale data, computational methods, and clearly articulated social-science theories to improve our understanding of society. Recent advances in machine learning, natural language processing
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. Collaborate with researchers from different institutions and disciplines, including engineering, computing, and clinicians. Participate in manuscript writing/preparation, conference talks/posters, manuscript
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, diet, and immune system to investigate their roles in various diseases, including but not limited to cancer, metabolic disorders, and infections. The research design involves the integration of large
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to the development of ongoing research. This will include the integration, modelling, and advanced statistical analyses of large genetic, ecological, and environmental data sets. The successful candidate is also