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on the interests of the candidate, they may also be involved in our ongoing developments of a digital data infrastructure for 2D materials (see 2dhub.org), which would involve high-throughput electronic structure
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, synthetic biology, data science and automation, and we collaborate closely with both academic and industrial partners. To strengthen our shared experimental infrastructure, we are establishing a central
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use cutting edge machine learning and data mining techniques to gain novel insights and advance our understanding of the rules defining T and B cell immunogenicity. If you are looking for the best
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on these problems, and use cutting edge machine learning and data mining techniques to gain novel insights and advance our understanding of the rules defining T and B cell immunogenicity. If you are looking
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Aarhus University with related departments. Contact information Before applying or for further information, please contact: Associate Professor Aurelien Dantan, +4523987386, dantan@phys.au.dk . Deadline
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to data from various sensors and radio signals? This is the main underlying theme to be explored within this postdoctoral position. The appointed researcher will investigate how AI embedded in physical
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. Consequently, your employment will as of that date be with a department. Contact information For further information, please contact: Assistant Professor Emil Laust Kristoffersen, +45 29271306, emillk
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cell biology, protein chemistry and mass spectrometry, molecular microbiology and biophysics. For further information about the position please contact Professor Brage Storstein Andresen, PhD, FRCPath, e
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Postdoctoral Researcher Position in Ecological Knowledge-Guided Machine Learning at Aarhus Univer...
on “Integrating AI into Aquatic Ecosystem Models to Decode Ecological Complexity” funded by Villum Fonden. Within that project, the focus is on exploring novel ways to infer information from environmental data
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will be part of a research environment focusing on integrating multi-source satellite remote sensing data and developing novel algorithms to quantify agroecosystem variables for environmental