46 data-"https:"-"https:"-"https:"-"https:"-"Inserm"-"Gustave-Roussy-" Postdoctoral positions at Aarhus University in Denmark
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Join us at the Department of Electrical and Computer Engineering at Aarhus University for a postdoctoral position focused on deep learning based analysis of remote sensing data for groundwater
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The Department of Ecoscience at Aarhus University invites applications for two postdoctoral positions to strengthen our research on image recognition, computer vision and deep learning applied
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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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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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unified data framework for microbial carbon dioxide conversion, integrating data from methanogens, acetogens, and hybrid projects for standardization, kinetic/thermodynamic measurements, and predictive
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reference If you want a referee to upload a letter of reference on your behalf, please state the referee’s contact information when you submit your application. We strongly recommend that you make
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Aarhus University with related departments. Contact information For further information, please contact: Prof. Anders Bentien, bentien@bce.au.dk, +45 30 36 95 15 Deadline Applications must be received
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autocracies. CULTS aims to produce the first-ever systematic analysis of the dynamics of authoritarian personality cults and their consequences. Utilizing, among others, the latest advances in text-as-data
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implement state-of-the-art data science principles into dental practice. While our primary focus is on the use of deep learning in (dental) imaging, our work expands into any type of data (e.g. tabular data
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different bioinformatic approaches to investigate and analyze omics data on immune response (central and peripheral) in people with Parkinson’s Disease, using both internally generated data as well as using