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11 Mar 2025 Job Information Organisation/Company Linköping University Research Field Physics » Applied physics Engineering » Materials engineering Researcher Profile Recognised Researcher (R2
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– such as taking statutory leave – then these may be taken into consideration. We are looking for candidates with a PhD in materials engineering, solid mechanics or a closely related research field. Good
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the described area (PhD thesis, publication record). Capacity for analytical and creative thinking. Enthusiasm, dedication and an ability to work both independently and in a team. Drive and independence. Written
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well as the Faculty of Science and Technology may also be included. The ability to manage personnel and operations is expected as a MIMS Group Leader and DDLS Fellow. The position is based in Umeå (but work in another
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the subject area may be included. Administrative tasks and assignments within the department and the Faculty of Medicine as well as the Faculty of Science and Technology may also be included. The ability to manage
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the subject area may be included. Administrative tasks and assignments within the department and the Faculty of Medicine as well as the Faculty of Science and Technology may also be included. The ability to manage
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-of-the-art cell and tumor biology methodologies, tissue analysis with histopathological techniques, and work with genetically engineered mouse models of cancer. Previous experience of conducting and analyzing
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/or other departmental duties, up to a maximum of 20% of full-time. We seek a motivated person that aligns with the following requirements of the position: A PhD Degree in Chemistry, Materials Science
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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The Division of Synchrotron Radiation Research (www.sljus.lu.se ), with more than 50 PhD students and
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are looking for candidates with a PhD in Computer Science, Visualization and Media Technology, Machine Learning or a closely related research field. A strong background in machine learning and visual data