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- Integreat -Norwegian Centre for Knowledge-driven Machine Learning
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, or experiences and understandings amongst different groups of citizens. A variety of analytical perspectives and qualitative methods are relevant, including public connection research, folk theory analysis
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of constructs and capsids using combinations of discriminative and generative methods, methods for protein structure prediction and docking simulations. The successful candidate should be able to develop new
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. Researchers in Integreat develop theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data. By combining the mathematical and computational cultures, and the
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groups of citizens. A variety of analytical perspectives and qualitative methods are relevant, including public connection research, folk theory analysis, critical algorithm studies, ethnography and
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. Excellent written and spoken English It would be advantageous for applicants to have Experimental experience with nanoparticulate formulations Experience of statistical and/or machine learning methods
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safe mitigation pathways for selected tipping elements in the Earth System. The candidate will be part of the Biogeochemistry Research Group at the Geophysical Institute, of the Carbon System Research
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elements in the Earth System. The candidate will be part of the Biogeochemistry Research Group at the Geophysical Institute, of the Carbon System Research Theme at the Bjerknes Centre for Climate Research
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technological progress in our increasingly digital, data- and algorithm-driven world. Integreat develops theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data
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the south of Norway. Responsibilities In this position, the candidates will work to develop new methods for accelerating the study of aging in commercial Li-ion battery cells and determining implications
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or more of the following empirical research methods will be considered an advantage: applied microeconometrics and causal inference; machine learning and data science. Experience with one or more of the