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on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
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intersection of machine learning and life sciences, developing next-generation models that improve our understanding of human biology and enable more proactive, personalized healthcare. As an Industrial PhD
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well as a highly collaborative research environment. For more information about Nathaniel Street’s research group, see: https://www.umu.se/en/staff/nathaniel-street/ Project description Establishing robust
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Are you interested in developing mathematically grounded methods for uncertainty quantification in deep learning, particularly for large language models in healthcare applications? Are you looking
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division The Division of Biomedical Engineering is part of the Department of Materials Science, and Engineering at the Ångström Laboratory. We perform research within the development of miniaturized
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Join MultiD Analyses AB and the University of Gothenburg to develop innovative bioinformatics and machine learning methods for RNA Fragmentomics, with the ambition to improve cancer care through
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The Center for Translational Genomics (CTG; https://www.ctg.lu.se /) is an open core facility at the Faculty of Medicine, Lund University, specialized in providing expertise and services in advanced Next
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KTH Royal Institute of Technology, School of Engineering Sciences Job description The AICell Lab (https://aicell.io ) in the department of Applied Physics at KTH and Science for Life Laboratory is a
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Gothenburg. We offer a creative and inspiring environment full of expertise and curiosity. The Karolinska Institute is one of the world’s leading medical universities. Our vision is to pursue the development
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cell-cell interactions to disrupt cancer-promoting equilibria. We aim to develop in silico and in vitro models and tools to build and validate digital twins of such interactions, with the objective