25 data-"https:"-"https:"-"https:"-"J" Postdoctoral positions at Nature Careers 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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In order to expand the Centre for Visual Data Science , the Department of Mathematics and Computer Science at the University of Southern Denmark, Odense, invites applications for a Postdoc position
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The Department of Agroecology at Aarhus University, Denmark, is offering a postdoctoral position in merging soil physics knowledge and data-driven modeling of soil processes and properties, starting
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within Ecological Data Science. Start date as soon as possible and end date 31st of December 2028. SustainScapes focuses on providing the scientific basis for rethinking landscapes for restoring
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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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Postdoc position to support international research and capacity-building projects employing elect...
of large-scale EM data for groundwater mapping. Teaching and training of Ethiopian partners and students in EM methods, data processing workflows, inversion software, and geological interpretation
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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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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