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PhD position: Global soil mapping with process-informed machine learning Faculty: Faculty of Geosciences Department: Department of Physical Geography Hours per week: 36 to 40 Application deadline
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Mathematics (Inverse Problems), Computer Science (Machine learning, Efficient Algorithms and High-Performance Computing), and Physics (Image Formation Modelling). Your project is part of the NXTGen High-tech
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Vacancies 2x PhD positions in the Mathematical Foundations of Machine Learning on Graphs and Networks Key takeaways The Discrete Mathematics and Mathematical Programming (DMMP) group
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Electrical Engineering, Computer Science, or a related discipline. A research-oriented attitude. Solid background in machine learning and optimization methods. Knowledge and experience in (wireless
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is looking for an aspiring PhD candidate to research causal machine learning and uncertainty quantification for Earth Observation time-series. Currently, predictive AI in Earth Sciences relies heavily
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manipulation, topological quests in magnetism, and applications for energy efficient computing? Join us as a PhD candidate to develop magnetic topology on demand! Information Topologically-protected conductors
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-physical systems secure and resilient in the presence of uncertainty and cyber-physical attacks? Then you may be our next PhD candidate in resilient and learning-based control of cyber-physical systems
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degree in AI, Computing Science, Mathematics, or Data Science. Strong coding, communication and organizational skills. Demonstrable experience with using machine learning packages (e.g., PyTorch
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, and/or machine learning. Preferably you finished a master in Computer Science, (Applied) Mathematics or related masters. Expertise in the field of visualization or visual analytics. You have good
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systems increasingly provide personalized recommendations in domains such as nutrition and lifestyle. However, many recommender and prediction systems rely heavily on opaque machine learning techniques