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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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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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humanities on AI safety and explainability. Reviewing technical literature on machine learning and explainable AI. Developing a normative evaluation framework for the use of explainable AI in machine vision
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network and data centers, the transmission link optimization at the physical layer, and the computing decentralized systems by exploiting state of the art machine learning approaches to ultimately implement
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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
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people's daily lives. Job requirements This work is at the intersection of between moral philosophy, human-computer interaction and industrial design. Candidates should have a master’s degree in a related
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advancements in computer graphics, leveraging techniques from the filmmaking industry to create visually compelling representations of complex brain datasets. Collaborate closely with renowned neurosurgeons
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systems on random graphs by rigorously characterizing the set of Gibbs measures. Applications of such problems range from computational complexity, coding theory, to machine learning. This PhD project
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(mean-field computations on bulk phase behavior and interfacial properties, colloidal interactions) and computer simulations. Of particular interest are designing molecules, particles, and interfaces
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similar lines and with all lessons learned from the 200-kV prototype. This project requires a unique skillset, ideally combining knowledge on pulsed power, high-voltage and power electronics. It will be a