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a balanced mix of AI with formal methods and testing techniques, we strive to make vulnerability detection more accurate, intelligent, explainable, and usable in practice. The project is cutting-edge
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minimizing the amounts of both false positives and false negatives. By combining static and dynamic analysis, using a balanced mix of AI with formal methods and testing techniques, we strive to make
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discrete-event systems, supervisory control theory, and formal methods to apply for the PhD position within the Supervisory Control group (see Group Supervisory Control ), which is part of the Control
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understandable explanations from machine learning models. We will achieve this together by creating the first mathematical framework for explainable AI and developing new explanation methods. This will involve
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methods. This will involve using tools from mathematical machine learning theory to prove mathematical guarantees about the performance of such new explanation methods, as well as programming to test out
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control synthesis methods. We invite highly motivated students with a strong background in discrete-event systems, supervisory control theory, and formal methods to apply for the PhD position within
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of positively and negatively charged polyelectrolytes will be the basis for a new method for the fabrication of membranes, requiring only water as a solvent. The porous structure of the membrane will be
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human perception and cognition. Experience in: experimental methods, computer rendering or VR/XR, programming. Affinity with multidisciplinary work, combining science, art, and technology. Enthusiasm
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PhD Position in Parsing and Formal Representation of Geographic Questions Faculty: Faculty of Geosciences Department: Department of Human Geography and Spatial Planning Hours per week: 36 to 40
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. We will achieve this together by creating the first mathematical framework for explainable AI and developing new explanation methods. This will involve using tools from mathematical machine learning