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predictive deep learning models, and physical mechanistic models (thermodynamic and kinetic models etc.). Examples of suitable backgrounds: machine learning, programming, mathematics, physics. You will
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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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multi-omics integration with advanced machine learning, including artificial neural networks, to predict disease-relevant splice variants across cardiometabolic diseases. By leveraging extensive meta
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