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motivated and skilled PhD candidate to work in the area of probabilistic machine learning. The position is fully funded for a term of four years. The research direction will be determined together
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computing (SC)? Are you fascinated by the emerging field of machine learning (ML)? Are you our next PhD-candidate in scientific machine learning or SciML (combining SC and ML)? Are you eager to work on the
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computing, advanced machine learning applications, and the deployment of 5G and future 6G systems, the size and traffic of data centers is steadily growing. The proliferation of data centers and the need
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of the vertices in multi-dimensional space for machine learning tasks. Policy evaluation is widely used in reinforcement learning, for instance, for training large language models. Your challenge is to develop
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machines are commonly used in electric vehicles. However, these machines have several disadvantages, such as the high cost of the permanent-magnet (PM) material and decreased performance at higher
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of machine capabilities. However, learning these models requires direct access to vast data repositories, which poses significant privacy and logistical challenges, especially in the health sensing domain
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. This PhD project is designed to identify, systemize and evaluate XAI methods, and to identify best-practices for machine vision in the context of autonomous driving, while taking into account human factors
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Motivated researcher, with a PhD in Mathematics, Computer Science, or related discipline(s). Ability to conduct high quality academic research, reflected in demonstratable outputs. Ability to teach
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Position PhD-student, (Post-doctoral) Researcher Irène Curie Fellowship No Department(s) Electrical Engineering Reference number V36.7438 Job description The Electrical Energy Systems (EES) group
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surrogate models. Applying symbolic reasoning to define and analyze system components and their interactions. Merging machine learning with symbolic AI to enhance system (performance) monitoring. Advancing