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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 support decision-making by integrating physical models and sensor data. These methods are validated through industrial case studies, with a particular emphasis on critical infrastructures where complexity
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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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national and international collaborations and access to world-class infrastructure. Lab website: https://www.sahu-lab.com/index.html Department of Molecular Medicine (IMM) The Department of Molecular
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) outlining a research project addressing the history of Danish botany and the Flora Danica volumes in the period 1840–1900 within the statement of future research plans and information about research
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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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members. Join us in making a positive impact on society! More information about the Department's research profile can be found here: https://www.communication.aau.dk. Qualification requirements Appointment
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with a background in either communication, journalism, political science, sociology, law, economics, or computational social science, as well as an interest in how AI transforms the media and information
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grant from the European Research Council. The primary challenge in powering AI chips and future data center systems is enhancing the efficiency of voltage regulator modules (VRMs) to meet their
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tasks include planning, conducting, and publishing epidemiological studies using large-scale observational data, primarily register-based, focusing on women’s short- and long-term health outcomes within