859 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" positions in Sweden
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Sweden’s national environmental objectives. Read more about the department: https://www.slu.se/ekologi Read more about our benefits and what it is like to work at SLU at https://www.slu.se/en/about-slu/work
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research - Analytical skill - Other documented knowledge or experience that may be relevant to doctoral studies in the subject. All applicants will be informed when the recruitment is completed. https
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distributed WaRM experiment (for details see here: https://onlinelibrary.wiley.com/doi/10.1002/ece3.9396). The employment is fulltime for three years. The deadline for applications is March 26, 2026 and the
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well as vendors of job advertisements. URL to this page https://web103.reachmee.com/ext/I011/853/main?site=7&validator=d7a66c13be778ef950c393a904293789&lang=UK&rmpage=job&rmjob=28613&rmlang=UK
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working at SLU by visiting: https://www.slu.se/en/about-slu/work-at-slu/ Location: Grimsö wildlife research station Form of employment: Fixed-term employment for 4 months, with a possible extension. Scope
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on the long term globally distributed WaRM experiment (for details see here: https://onlinelibrary.wiley.com/doi/10.1002/ece3.9396 ). The employment is fulltime for three years. The deadline
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to contribute to a positive work environment. We also value the ability to work independently in carrying out work tasks, as well as openness to learning new skills and taking on new responsibilities. We value
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technology, and to build a Swedish quantum computer). Within AQP, the group of Anton Frisk Kockum has the overarching goal of providing humanity the tools to understand and use large quantum systems. Working
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data-driven methods relying on machine learning, artificial intelligence, or other computational techniques. The applicant is expected to develop and apply data-driven and machine learning-based methods
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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