186 machine-learning "https:" "https:" "https:" "https:" "RAEGE Az" PhD positions in Sweden
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participates in approximately 30 courses at undergraduate and advanced level. Read more about the department here (https://www.slu.se/en/departments/soil-environment/ ). And about our benefits and what it is
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Instructions on how to apply Applications shall be written in English and be compiled into a PDF-file containing: CV, including a list of publications, Where to apply Website https://lu.varbi.com/en/what:job
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ecology and in ecosystem-based management. As a person, you are responsible and good at communicating. You enjoy working in a group and demonstrate capacity for learning new things, working systematically
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is to advance the understanding of forest ecosystems and how these should be managed today and in the future. For more information: http://www.slu.se/en/departments/forest-ecology-management/ Read more
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. Your profile You have documented expertise in marine ecology and computer vision and machine learning methods for video-based fish monitoring. You have excellent IT skills and experience in handling
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today and in the future. For more information: http://www.slu.se/en/departments/forest-ecology-management/ Read more about our benefits and what it is like to work at SLU at https://www.slu.se/en/about
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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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undergraduate and advanced levels, mainly in programmes leading to degrees in agronomy and biology. Read more about our benefits and what it is like to work at SLU at https://www.slu.se/en/about-slu/work-at-slu
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certificate, and other certificates/grades that you wish to be considered. Where to apply Website https://lu.varbi.com/en/what:job/jobID:886099/type:job/where:39/apply:1 Requirements Research
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access to preventive care and neighborhood characteristics influence long-term health trajectories. The project applies both econometric and machine learning approaches to identify high-risk groups and to