16 data-"https:"-"https:"-"https:"-"UCL" Postdoctoral research jobs at Aarhus University in Denmark
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Mechanics and Turbulence” group and conduct research on data-driven techniques for turbulence modeling in LES and RANS. The initial contract will be for one year, with the possibility of an additional one
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The Section for Electrical Energy Technology at the Department of Electrical and Computer Engineering (ECE), Aarhus University, is in a phase of rapid growth in both education and research
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sets, lexicon development, use of instrumental techniques to correlate or predict sensory characteristics and multivariate data analysis. This position is part of an interdisciplinary research project
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? Then the Department of Electrical and Computer Engineering invites you to apply for a 2 year postdoc position bridging research with industrial implementation and innovation. Expected start date and
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description We invite applications for a 2-year postdoctoral position with the possibility of extension. The successful candidate will lead experimental campaigns and data analysis to quantify greenhouse gas
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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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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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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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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
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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