16 software-engineering-model-driven-engineering-phd-position PhD scholarships at Linköping University in Sweden
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application! We are looking for a PhD student in Visualization Technology and Methodology with a focus on interactive visualization, visual learning, science communication, and educational science, formally
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verifiability for AI systems, based at the Department of Computer and Information Science. These positions are funded by the Wallenberg AI, Autonomous Systems and Software Program (WASP). Wallenberg AI
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and Information Science. These positions are funded by the Wallenberg AI, Autonomous Systems and Software Program (WASP). Wallenberg AI, Autonomous Systems and Software Program (WASP) is Sweden’s
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employment https://liu.se/en/work-at-liu/vacancies/27309 Your work assignments The PhD position is part of an interdisciplinary research project called “c/o Glass - making flat glass circular in the built
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of Wnt Signaling using model organisms and/or organoids systems. Your workplace The position is located at the Division of Molecular Medicine and Virology (MMV) which is a part of the Department
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application! For contributing to a more sustainable and circular future, and at the same time getting a PhD – consider applying for this position! Your work assignments The PhD position is part of
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Number 853--1-27417 Is the Job related to staff position within a Research Infrastructure? No Offer Description The Division of Biology at Linköping University invites applications for a four-year PhD to
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mathematics. The applicant should be skilled at implementing new models and algorithms in a suitable software environment, with documented experience. Experience in applying or developing machine learning
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behaviours evolve is a long-standing goal in evolutionary biology. Using the domestic dog as a model species, the PhD student selected for this project will investigate unanswered questions on how complex
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application! Your work assignments We are looking for one PhD student working on generative AI/machine learning, with applications towards materials science. Generative machine learning models have emerged as a