174 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" "Univ" scholarships in Sweden
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dimensions andanalyse particle trajectories using a combination of established tracking algorithms and machine-learning-based approaches. You will further correlate the diffusive behaviour of viruses
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. Where to apply Website https://su.varbi.com/en/what:job/jobID:912971/type:job/where:39/apply:1 Requirements Research FieldChemistryEducation LevelMaster Degree or equivalent Research FieldChemistryYears
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advertisement, and that it is submitted before the deadline. The instructions for applicants are available at: How to apply for a position . Where to apply Website https://su.varbi.com/en/what:job/jobID:912959
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/tensions between the global North and global South. We will also consider applicants focused primarily on Swedish/Nordic cases or topics. For full information of the five REMESO research streams see: https
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) , was created. The Department of forest genetics and plant physiology is part of Umeå Plant Science Centre (UPSC, https://www.upsc.se ) which is a centre of excellence for experimental plant research and forest
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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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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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/tensions between the global North and global South. We will also consider applicants focused primarily on Swedish/Nordic cases or topics. For full information of the five REMESO research streams see: https
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experience) of their research career and must not have been awarded a doctoral degree. Where to apply Website https://umu.varbi.com/en/what:job/jobID:907734/ Requirements Research FieldBiological
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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