318 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "U.S" research jobs in Sweden
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the official date of completion. Exceptions to the three-year eligibility limit may be made for documented circumstances such as parental leave, sick leave, or military service. The following experience will
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manufacturing. It is meritorious to have previous experience in data analysis and processing with Python (or similar), preferably including documented experience with machine learning tools. It is meritorious
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April 1st 2026 or by agreement. For more details about the position and the application procedure see: https://www.umu.se/en/work-with-us/open-positions/postdoctor-2-years-in-organic-electronics-with
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infrastructure here: https://www.slu.se/institutioner/molekylara-vetenskaper/ Read more about our benefits and working at SLU by visiting: https://www.slu.se/en/about-slu/work-at-slu/ Location: Uppsala Form
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what it is like to work at SLU at https://www.slu.se/en/about-slu/work-at-slu/ Location: Uppsala Form of employment: Temporary employment 24 months Scope: 100% Start date: By agreement (preferably spring
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Machines, (IMOL), Poland, and the Leicester Institute of Structural and Chemical Biology, United Kingdom. Your work may include clinical and biomedical projects. It may also include technique development
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Machines, (IMOL), Poland, and the Leicester Institute of Structural and Chemical Biology, United Kingdom. Your work may include clinical and biomedical projects. It may also include technique development
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that support the unit for area protection and marine spatial planning, as well as operations at SLU Aqua. Your profile You have documented expertise in marine ecology and computer vision and machine learning
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Postdoctoral Position and Postdoctoral Fellowship (2yrs) in Intracellular Host-Pathogen Interactions
proteomics analyses. APPLICATION PROCEDURE Please note that the application procedures differ between the two postdoc projects. More information about the application procedures is provided here: https
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. The primary objective is to develop computational methods, using deep learning–based protein design, for the successful design of 2D lattices. These methods will then be applied to generate designs targeted