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computing, manifold learning, geometric data analysis, or phylogenetic models of trait evolution Ability to collaborate and contribute to an interdisciplinary research environment. Scientific originality
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, incorporating their own ideas and experience in computer vision, machine learning, and related fields, to further visualization and interpretation of molecular images. Our research environment focuses
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, physical and chemical characterization, use of LSRI facilities, and in vitro evaluation. The project is a collaboration between KTH, RISE, Larodan, and Karolinska Institutet. The position will be shared
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collaborate closely with research groups to ensure our solutions are stable, user-friendly, and future-proof. You will join a growing team with skilled system developers. In this role, you will: Develop and
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of complex brain processes. The prospective PhD candidate collects brain MSI data and develops novel machine learning methods in connection to generative models such as flow matching. Therefore, the doctoral
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the University of Gothenburg, spans the breadth of computing disciplines. Our internationally visible research, strong industry links and diverse environment create a collaborative setting where ideas
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and conditions for Open Science and FAIR, as well as the application of these. Documented experience of leading collaborative projects. Skills in coordination and communication, and the ability
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are seeking a candidate who is detail-oriented, quick to learn new skills, and able to work both independently and collaboratively. In addition to the qualification requirements listed above, we highly value
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provide high-quality bioinformatics support in collaborative validation studies, ensuring that SciLifeLab’s proteomics infrastructure remains a cornerstone in precision medicine. Qualifications Requirements
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of the recent years, with pioneering research in the life sciences and extensive national and international collaborations. The Assistant Professor will be a key player in this dynamic research environment