181 machine-learning "https:" "https:" "https:" "https:" "https:" "Bournemouth University" scholarships in Sweden
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creative and stimulating environment with the opportunity to network with both the business community and international contacts. Read more about our benefits and what it is like to work at SLU at https
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below. Your application, including diplomas, must be written in English or Swedish. Closing date for applications: April 21, 2026 Reference number: 2788-2026 URL to this page https://web103.reachmee.com
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at https://www.slu.se/en/about-slu/work-at-slu/ Description: We are looking for a PhD to focus on separation of fat and proteins from dairy side streams and plant-based extracts. The project is a
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the Arctic, experimental tests of climate driven changes in carbon export from land and turnover and release of greenhouse gases (CO2 and CH4 ) from headwaters, and use of machine learning and process-based
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their potential. Join us on our journey in finding new ways to work, pioneering cutting-edge methods and bringing unexpected teams together! How to apply? Read more and apply at: https://astrazeneca.eightfold.ai
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management and career development. Where to apply Website https://www.hb.se/en/about-ub/work-at-ub/how-to-apply-for-a-job-with-us/ Requirements Research FieldTechnology » Materials technologyEducation
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to apply Website https://www.hb.se/en/about-ub/work-at-ub/how-to-apply-for-a-job-with-us/ Requirements Research FieldTechnology » Materials technologyEducation LevelMaster Degree or equivalent Skills
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more about our benefits and what it is like to work at SLU at https://www.slu.se/en/about-slu/work-at-slu/ Development of statistical methods for estimating plant population size and change Mathematical
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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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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