45 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" research jobs at Aarhus University in Denmark
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underlying greenhouse gas fluxes Support training of young researchers in using biogeochemical observations and data analysis Write and contribute to international peer-reviewed publications Contribute
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include psychiatric disorders as well as clinical and social outcomes, but specific tasks may depend on applicants. The positions will generally involve various data analyses using Danish register data and
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Electrophysiological characterization of muscle fiber excitability (in collaboration with the research group) In vivo studies using animal models of neuromuscular disease Integration of molecular and transcriptomic data
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to diversity and inclusion, and success in mentoring. Place of work The place of work is the Department of Food Science, Aarhus University, Agro Food Park 48, 8200 Aarhus N, Denmark. Contact information
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description You will be contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will
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reactors Maintain detailed records of experimental data, process conditions, and system modifications. Publish scientific articles based on data collected during the research, development, and innovation
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graph algorithms for optimization under physical constraints Applying graph mining and graph data management techniques Designing computational methods for waste heat reuse and green transition goals
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and applying genetic and genomic approaches to biodiversity research. This includes integrating environmental DNA (eDNA) and molecular tools with ecological data to enhance our ability to assess
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Applications are invited for a 23-month research assistant position in the field of Aerial Robots at the Department of Electrical and Computer Engineering, Aarhus University, Denmark. Expected start
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be developed and implemented in the GEOS-Chem chemical transport model, coupled to the Community Earth System Model. Standardized large wildfire events will be simulated based on historical data and