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Experience with machine learning, data mining and data assimilation is a plus Knowledge of git, docker, kubernetes, and/or metadata is a plus Ability to work within a team Excellent interpersonal and
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and control of windfarms For this position the research activity is expected to develop new methods in robust and physics-informed machine learning, reduced-order modelling, data-driven nonlinear model
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The place of work is Aarhus University, Campus Ole Worms Alle 3, 8000, Aarhus C. The affiliation will be with Land-CRAFT, Department of Agroecology. More information can be obtained from Assistant Professor
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The place of work is Aarhus University, Campus Ole Worms Alle 3, 8000, Aarhus C. The affiliation will be with Land-CRAFT, Department of Agroecology. More information can be obtained from Assistant Professor
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: A small, international team with open communication and flat hierarchies Specializes in machine learning and data mining for plant genomics Committed to interdisciplinary work combining genetics, data
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of agricultural in dry lands and marginal environments, animal value chain, water and energy sustainability, biorefinery and bioenergy, cultivation and conversion of micro and macroalgae, and geology and mining
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 10 hours ago
Posting Information Posting Details Department Neurology - 411801 Posting Open Date 05/16/2025 Application Deadline Open Until Filled Yes Position Type Postdoctoral Scholar Position Title Post-Doc
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, modeling and analysis, integrating diverse data sets to identify global risks affecting sourcing strategies. In this role you will: Conduct and contribute to research and model development to enhance
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Postdoctoral position Posting Date: 7/29/2024 Yale School of Medicine Department of Emergency Medicine The Data Mining (DM) Lab at Yale University lead by Dr. Samah Fodeh has an opening for a
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weather instrumentation data analysis (lidar, radar, weather station, radiometer, sonde, cameras) Experience with mining and cleaning “big” observational data and/or online databases Experience with