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data storage in Iceland. Drawing on science and technology studies and, specifically, infrastructure studies (Edwards et al 2009; Star 1999), the project aims to follow moments of instability and re
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barriers and facilitators of prevention programs. The ideal candidate has a background in or experience with one or more of the following topics and areas: Survey methodology / Survey data analysis
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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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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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to assess cell performance evolution and degradation behaviour Investigating impurity-induced degradation mechanisms related to feed gases, system components, or cell materials Analysing experimental data and
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the relevant union. The period of employment is 3 years. You can read more about career paths at DTU here . Further information Further information may be obtained from Associate Professor Timothy P. Jenkins
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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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] that process information in temporal rather than spatial modes to reduce their footprint. The project involves a collaboration between DTU Electro (Senior Researcher Mikkel Heuck) and Harvard University (Dr
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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
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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