15 phd-position-in-material-engineering-phd-polymer-composite PhD positions at Linköping University in Sweden
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2 Sep 2025 Job Information Organisation/Company Linköping University Research Field Engineering » Materials engineering Chemistry » Applied chemistry Physics » Applied physics Researcher Profile
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tailored materials design with advanced characterization methods to enable new device functionalities. The research aims to expand the capabilities of organic electronic devices by integrating light
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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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application! We are looking for a PhD student in Medical Science. Your work assignments Your work will entail the investigation of how Wnt signaling, a cell-to-cell communication pathway conserved across
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application! We are looking for up to two PhD students in trustworthy machine learning, with a particular focus on cybersecurity, privacy, and verifiability for AI systems, based at the Department of Computer
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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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, interview, writing skills, and the candidate’s written motivation for seeking the position. Your workplace The PhD student will work at The Division of Biology at the Department of Physics, Chemistry and
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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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materials. This class of materials has unique properties which make them promising candidates for next-generation electronic devices, energy storage systems, sensors, and catalysts. However, they also pose
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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