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We are seeking highly motivated and talented applicants for a 1-year postdoctoral position (with the possibility of 1-2 year extension) in the area of efficient foundation model inference. Join us
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productivity and food web. The work will consider the impacts of stressors to marine systems, especially related to offshore wind farms, and how they propagate through and disrupt the food web. You will be a
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complement the Department’s ongoing activities in landscape-scale modelling, with a specific focus on nature-based solutions in agricultural landscapes. The successful candidate will be integrated
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Postdoctoral Researcher Position in Ecological Knowledge-Guided Machine Learning at Aarhus Univer...
ecosystem models to evaluate if the respective hybrid MCL models are improving their performance. The overall project aim is on refining current aquatic ecosystem models by building models based on KGML
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for the ammonia synthesis. We are currently further pursuing these systems continuing these fundamental surface science experiments- see SCIENCE 383 (2024) 1357- but are also aiming at realising high area materials
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The Department of Environmental Science at Aarhus University (AU-ENVS), Roskilde, invites applications for a 3-year position as postdoc in atmospheric modelling to carry out research on large
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, the developed models will be deployed in realistic scenarios, including turbulent flows over complex terrain, within built environments, and in wind farms. The project integrates fundamental applied mathematics
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is expected to start 1st of February 2026 or as soon as possible and will end 31st of December 2028. Job description The postdoc position is focused on understanding spatiotemporal current patterns
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areas: chromatin/epigenetics (e.g., ChIP-seq, CUT&RUN/CUT&Tag, chromatin fractionation), stem cell culture and differentiation (ideally mouse ESCs), reporter-based assays, flow cytometry/FACS, and/or
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description You will be contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will