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, spectroscopic signatures, microstructural images, processing conditions, and macroscale performance will be used for the optimization of materials. The candidate will collaborate extensively with in
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work closely with fellow PhD students and postdocs at Chalmers, and collaborate with academic and industrial partners in Sweden and internationally. The role also offers opportunities for travel and
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: · A vibrant and collaborative research environment, supported by experienced supervisors with expertise in wildlife ecology, conservation technology, applied statistics, and deep learning · Access
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. Participate in training events, workshops, and conferences organized by the SOCIAL Doctoral Network. Collaborate with international supervisors and peers. Contribute to departmental activities, including
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and School, Working Life, and Teacher Education in Collaboration, funded by the Swedish Research Council and conducted in cooperation with other higher education institutions. The overarching aim
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collaborative skills, motivation and independence, as well as how the applicant through his/her experience and competence is judged to have the abilities necessary to develop within and acquire the doctoral
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quantitative or computational approaches are required. Prior experience with image analysis, machine learning, signal processing, or structural biology is meritorious but not mandatory. Excellent written and
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medical applications. Federated Bayesian learning offers a solution to those problems by allowing multiple participants to train machine learning models collaboratively, without sharing any data. Bayesian
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numerical analysis, computational fluid dynamics, and uncertainty quantification with diverse applications. Our group maintains active collaborations with other divisions at Linköping University and broader
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organisation. Located in Värmland, a scenic region with a rich cultural life, we are committed to promoting sustainable development in close collaboration with the wider community. Karlstad University has a