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research sections with around 320 highly dedicated employees, of which approximately 50% are scientific staff. More information can be found here . We believe in encouraging inclusion, acceptance, and
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research sections with around 350 highly skilled employees, of which approximately 50% are scientific staff. More information can be found here . We believe in encouraging inclusion, acceptance, and
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analysis) Data collection, documentation, and basic data analysis Contribution to reporting, presentations, and potentially scientific publications Supporting collaboration within the research group and with
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the implementation of PT in healthcare: unknown mechanisms of action; lack of clinical gold standards; legal/regulatory obstacles; and ethical/societal challenges. More information can be found on the INTEGRATE home
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the agricultural pest, Spotted Wing Drosophila in current and future climates. This includes exploring mechanistic distribution models involving experimental data on environmental tolerances such as
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hardware-in-the-loop testing of integrated energy systems. The candidate is expected to have a solid understanding of system monitoring, experimental data management, and validation of thermal systems, as
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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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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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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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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