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to fluorescently labelled AP-L complexes and analyze how the cells accumulate and spread the complexes over time using live cell imaging, immunocytochemistry, ELISA, Western blot, electron microscopy and other
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is part of the national research programme DDLS. Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels
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part of the role. Requirements To meet the entry requirements for doctoral studies, you must been awarded a second-cycle degree in Synthetic Molecular Chemistry , or have completed at least 240 credits
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details of the research project are decided in a dialogue between the doctoral student and the supervisor. The student will be part of the WASP graduate school. The graduate school within WASP is dedicated
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recruited early-stage researcher will also be a part of the WISE Graduate School (https://wise-materials.org/research/graduate-school/ ), an ambitious nationwide program of seminars, courses, research visits
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, computer vision, and AI for science. The exact details of the research project are decided in a dialogue between the doctoral student and the supervisor. The student will be part of the WASP graduate school
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This doctoral project is carried out in collaboration with the Centre for Preventive Psychiatry. The project is part of an interdisciplinary initiative to explore concepts of psychiatric illness, notions of risk
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active co-workers. Read more about us here About the division The Division of Nanotechnology and Functional Materials is part of the Department of Materials Science and Engineering, Ångström Laboratory
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of the results Development of the methods You will be given extensive creative space to develop the project, significant resources to produce cutting edge results, and you will profit from being part of our highly
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University Duties This PhD project is part of the AFLOW consortium supported by the Swedish Energy Agency and focuses on multi-scale modelling of aqueous organic redox flow batteries, to build a predictive