17 learning "https:" "https:" "https:" "https:" "https:" "https:" positions at SciLifeLab
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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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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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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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a doctoral student with a strong background in machine learning, mathematics, and modeling, and an interest in biological systems. The successful candidate will join a project to understand and model
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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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candidates to be interested to learn these practical skills. While the project has and initial plan with funding from the Swedish Research Council (see below), the different parts of the project will be
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multi-omics integration with advanced machine learning, including artificial neural networks, to predict disease-relevant splice variants across cardiometabolic diseases. By leveraging extensive meta