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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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statistics, engineering physics, physics, machine learning, or in a similar subject, or have completed at least 240 credits in higher education, with at least 60 credits at Master’s level including
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, longitudinal patient and population registries and biobanks. Project description Large language models (LLMs) enable the extraction of clinical information from unstructured medical text. However, current LLM
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computer science, image analysis and machine learning, engineering physics, data science, applied mathematics, molecular biotechnology engineering, or another related field; or Have completed at least 240
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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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revised and extended based on current developments and results. We expect the student to take an active role in project development. About the employment The employment is a temporary position according to
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prediction, most current models still describe proteins largely as static structures and do not fully capture the conformational ensembles that underlie protein function. This PhD project aims to address