89 algorithm-development-"Multiple"-"Simons-Foundation"-"Prof"-"University-of-Wisconsin-Madison" positions at Aarhus University
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The Department of Biomedicine at Faculty of Health at Aarhus University invites applications for a position as Postdoc in the field of Cancer immunology/multiple myeloma as per 1 December 2025 or as
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, cognitive science, art history, and creativity studies exploring digital interfaces that facilitate collaboration between humans and AI algorithms. We are located at the Department of Management at Aarhus BSS
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standard fine-tuning. Key research objectives include: Developing efficient algorithms: exploring and designing training strategies (e.g., supervised finetuning, reinforcement learning, or new alignment
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funding affect the subsequent performance of firms and scientists, in terms of outputs such as the number of papers, products, patents, etc. (Can an optimal applicant template be developed by training
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. Finding solutions requires studies that extend across multiple spatial and temporal scales. Land-CRAFT (https://land-craft.dk/ ), established in 2022, brings together experts on climate impact research and
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develop ECE at Aarhus University. The announced position will support the strategic agenda of the university by extending the competencies in ECE within the Electrical Energy Technology section , including
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system. Researchers with experience in neuroscience, microbiology and immunology are welcome to apply, but it is expected that you will span multiple of these fields once established in the lab. You will
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variability, and disturbances (e.g., droughts, floods, wildfires) influence carbon sequestration, GHG fluxes, and ecosystem stability in restored wetlands. Your work will contribute to the development of robust
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motivated candidate, who will develop and support projects at the intersection of soft matter physics and food science. The Postdoc position is part of the Food technology group at the Department of Food
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better and faster decisions when assessing funding applications, ensuring the efficient and unbiased elimination of poor applications? This question can be addressed through training algorithms on past