879 machine-learning "https:" "https:" "https:" "https:" "https:" "The University of Edinburgh" positions in Sweden
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addition to conventional software, the scope includes engineering of AI enabled systems (primarily ML and LLM), and thus MLOps (Machine Learning Operations), datacentric AI, and legal and ethical aspects of AI
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solutions across the natural sciences. Your workplace You will be employed at the Department of Mathematics in the Division of Applied Mathematics, https://liu.se/en/organisation/liu/mai/tima . The research
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promote sustainable agriculture. More about the Department: https://www.slu.se/en/departments/ecology/ More about working work at SLU: https://www.slu.se/en/about-slu/work-at-slu/ Location: Uppsala, Sweden
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difference! For further information, please visit: https://www.lunduniversity.lu.se/about-lund-university/work-lund-university www.sweden.se https://www.maxiv.lu.se/about-us/careerjobs/comp_and_benefits
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to make a real difference! For further information, please visit: https://www.lunduniversity.lu.se/about-lund-university/work-lund-university www.sweden.se https://www.maxiv.lu.se/about-us/careers
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like to work in a challenging and supporting environment? Then join us and take the opportunity to make a real difference! For further information, please visit: https://www.lunduniversity.lu.se/about
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working conditions and attractive benefits. Equality, diversity and equal opportunities are essential to quality and form an integral part of KTH’s core values as a university and public authority. Learn
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international research collaborations to generate cutting-edge research and outreach to further ecology as science and promote sustainable agriculture. More about the Department: https://www.slu.se/en/departments
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from Hi-C and Capture Hi-C experiments. Have experience developing graphical user interfaces (GUIs). Candidates with knowledge or experience in machine learning methods will be prioritized. Successful
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Initiatives in Forest Research (WIFORCE) program. The successful applicant will work on the development of bioacoustic monitoring methods using automated recording units (ARUs), deep learning methods, and