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higher education credits (ECTS). Relevant courses include, for example, image processing, computer vision, machine learning, deep learning and neural networks, as well as courses in Python, GPU programming
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software or similar languages and experience with modern machine learning and deep learning frameworks parallel computing using clusters like UPPMAX and GPUs for high-performance computing and parallel
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research experience in e-health, digital health or a related field experience of, or a documented interest in, machine learning, AI methods or large language models (LLMs) in clinical or health-related
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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