-
, molecular communication, and systemic molecular networks, and to identify how these processes change across physiological and disease states. Through this work, you will develop predictive and biologically
-
enjoys a wide network of strong international collaborators all around the world, for example at the University of Oxford, the University of Melbourne, and the University of California, Los Angeles. We
-
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
-
multi-omics integration with advanced machine learning, including artificial neural networks, to predict disease-relevant splice variants across cardiometabolic diseases. By leveraging extensive meta
-
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
-
, machine learning, data integration, and scientific communication. As part of the DDLS Research School, the student will also participate in national courses, seminars, and networking activities within data