31 learning-"https:"-"https:"-"https:"-"https:"-"https:" "https:" PhD positions in Denmark
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calls. New research areas may be added until the application deadline. Beyond the research conducted during the PhD project, a successful candidate is expected to teach three to four hours weekly during
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projects Actively participate in PhD courses Write scientific articles and finalize your PhD thesis Participate in international meetings Conduct a research stay at an institution abroad Teach and
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career step. At HST, our values – Share. Care. Dare. – shape the way we collaborate, support one another and create knowledge with societal impact. Learn more about the department on our website and in
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Learning models to understand and predict interactions in dynamic ecological networks. Our lab is looking for candidates for the following stipend: Learning the Structure and Dynamics of Complex Networks We
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advanced analytical approaches, including deep learning and machine learning, to improve disease subtyping and risk prediction. You should have a strong willingness to learn, enjoy tackling challenging
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: fluctuating renewable energy, dynamic electricity prices, and increasing system complexity. In the future, industrial energy systems will not simply run. They will understand themselves. They will learn from
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, statistics, data science, and public health. The goal is to develop new methods that allow researchers to learn from sensitive health data without compromising individual privacy. Using unique, nationwide
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pharmacology, to better understand the complex interplay of the many factors that drive cardiometabolic disease. You can learn more in the Executive Summary of CBMR's Strategy 2024–2028 . CBMR was established in
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has more than 600 students in its BSc and MSc programs, which are based on AAU's problem-based learning model. The department leverages its unique research infrastructure and lab facilities to conduct
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focuses on the development of secure and trustworthy AI for resource-constrained embedded systems. The research will investigate how machine learning models can be designed and deployed efficiently