75 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" research jobs at Nature Careers in Denmark
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19165 Post-Doctoral Fellowship in risk assessment and prioritization and remediation of dumped mu...
fishing activities, major shipping routes, and offshore development locations. The EU Oceans Pact highlight the need to assess and manage dumped munitions. Two EU-funded projects, MUNI-RISK (https://muni
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: Application (cover letter) Vision for teaching and research for the tenure track period CV including employment history, list of publications, H-index and ORCID (see http://orcid.org/ ) Teaching portfolio
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5-year undergraduate nanotechnology programme and nanoscience graduate programme (https://phd.nat.au.dk/programmes/nanoscience/) the center provides a full educational environment. In
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application of magnetically enhanced electrocatalysis for water splitting and CO2 reduction (see e.g. https://doi.org/10.1038/s41560-019-0404-4). Your main tasks may include Application of new materials and
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personality. The Department of Biology The Department of Biology (http://bio.au.dk/ ) provides a framework for research and teaching in all major biological subdisciplines. The department is especially known
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Applications are invited for a postdoctoral position in the group of Dr Aleksandr Gavrin ( https://mbg.au.dk/a-gavrin/ ) at the Department of Molecular Biology and Genetics, Aarhus University
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will develop atomistic models and machine-learning potentials to interpret experimental data and predict catalytic performance. The tasks can include Advancing equivariant neural network potentials
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analysis to translate THz signals into optical material properties such as refractive index and absorption coefficient. Development of machine learning algorithms for material classification. Exploration
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key agroecosystem variables. These variables include cover crop growth, crop nitrogen, yield, and tillage practices. You will develop novel algorithms to integrate data-driven machine learning and
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biogeochemical modelling and data-driven machine learning approaches at an ecosystem scale to improve our understanding of the fate of nitrogen fertilizers applied to agricultural soils. This understanding will be