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background in Machine Learning, with the ability to understand and extend current research Solid programming and engineering skills Comfortable working with modern development tools and practices Strong
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biomedical engineering, electrical engineering, machine learning, statistics, computer science, or a related area considered relevant for the research topic, or completed courses with a minimum of 240 credits
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for cardiometabolic and liver diseases. Current therapeutic strategies largely operate at the gene level, overlooking the functional diversity generated by alternative splicing. This project addresses this critical gap
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methods (e.g. machine learning methods and many other methods) to harmonize historical and current pathogen nomenclature, standardize laboratory test methods and result vocabularies, and translate clinical