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Field
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develop, implement and validate state-of-the-art machine learning methods, ranging from deep learning and transformer architectures to unsupervised learning and representation learning approaches. You will
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of interests and expertise. This will include data management, co-design of representations, and further development of a knowledge platform that supports knowledge exchange, citizen science and
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and transformer architectures to unsupervised learning and representation learning approaches. You will work in close collaboration with clinicians, AI researchers, statisticians, and industrial
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make better-informed decisions. For this purpose, PATH2ZERO aims to develop a digital twin. The main goal of this data-driven virtual representation of the inland waterway transport system is to assess
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interest and experience in investigating neural and psychophysiological processes of motivation, effort mobilization, and affective experience. Applicants should bring a sound knowledge of quantitative and
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in one of the following areas: Machine Learning / Information Retrieval / Knowledge Graph Representation / Recommender Systems Graph Theory/Network Science Python, and up-to-date machine learning
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/PhD studies in the field of Nutritional Sciences or related disciplines (e.g., life sciences) Outstanding dissertation Lively interested in science Very good knowledge in nutritional human physiology
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, bioengineering, or a related field Strong interest in interdisciplinary research and novel approaches to scientific collaboration Experience in science communication, public engagement, outreach, knowledge
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, Differential Geometry, Mathematical Biology, Number Theory, Representation Theory, Topology, and Probability. The department has a thriving Ph.D. program that attracts students from across the globe and offers
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an in depth knowledge of a specialized field, process, or discipline and may involve organizing and implementing complex research plans, the development of methods of research, testing and data collection