144 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" Postdoctoral positions in Denmark
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. We believe that cooperation and collegiality are essential elements for research successes. You can find more information about the team and our work here . The project You will join the Lund Group
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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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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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. The work will explore adaptive MAC designs that leverage waveform structure and context information to improve user management, access efficiency, and robustness. A key aspect of the research is the joint
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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 Matteo Fumagalli
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career paths at DTU here . Further information Further information may be obtained from Professor Rasmus Bjørk, +45 46 77 58 95, rabj@dtu.dk . You can read more about DTU Energy at www.energy.dtu.dk
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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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. Further information Further information may be obtained from Associate Professor Xiaodong Liang (xlia@kt.dtu.dk ) and Professor Georgios M. Kontogeorgis ( gk@kt.dtu.dk ). You can read more about DTU
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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