883 machine-learning "https:" "https:" "https:" "https:" "UCL" "UCL" positions in Sweden
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- University of Gothenburg, Department of Education, Communication and Learning
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percent of working hours Contract terms The Doctoral student positions is fully funded from start. The position is a fixed-term appointment of four years, with the possibility to teach up to 20%, which
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. The department will provide support with language learning. Eligibility The applicant must meet the following qualification requirements: PhD or equivalent academic qualifications research expertise in
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experience in manufacturing systems modeling, simulation (i.e., DES), and digital twins. • Good knowledge and experience in machine learning, reinforcement learning, and AI-based optimization for production
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, such as pulse design or numerical optimization Background in data-driven or machine-learning approaches relevant to optimal control (e.g., model learning, reinforcement learning) What you will do Take
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at https://nbis.se. Duties We are seeking a candidate who wants to help enable life science research in Sweden that goes beyond what is achievable by individual researchers, a single university, or a single
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lecturer who can complement our team with expertise in gender studies that is clearly oriented towards global studies and/or human rights, and who can teach, supervise and communicate with students in
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in English. Mark your application with the reference number below. Closing date for application: 13 April 2026 Reference number: 2464-2026 URL to this page https://web103.reachmee.com/ext/I003/583/main
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within data-driven Life Science research. More information about NBIS can be found at https://nbis.se . Duties We are looking for someone who wants to help enable life science research in Sweden that goes
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-driven Life Science research. More information about NBIS can be found at https://nbis.se. Duties We are seeking a candidate who wants to help enable life science research in Sweden that goes beyond what
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and CH4) from headwaters, and use of machine learning and process-based model for large scale assessments and projections of the land-water carbon cycle to variation in climate conditions. The detailed