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developing new explanation methods. This will involve using tools from mathematical machine learning theory to prove mathematical guarantees about the performance of such new explanation methods, as
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elements/agents, adjusting expectations, mechanisms of contagion dynamics, feedback loops, and non-linear tipping of system dynamics. The SPHINX research program aims to fundamentally advance simulation
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. The project will have a strong quantitative component relying on computer science, machine learning, policy evaluation and causal inference. In addition, it will also contain a strong business component, in
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and integrates two crucial elements for the breakthrough and impact of Additive Manufacturing: Standardization and Intellectual Property (IP). Aiming for both theoretical and practical contributions
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training programme will be set up reflecting your training needs and career objectives. About 20% of your time will be dedicated to this training component, which includes following courses and/or workshops
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transformations in view of the targeted mechanical properties. These phase transformations occur during the metal forming processes in which they are shaped to a product. These need to be well controlled
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of synthetic chemistry, biochemistry and microbiology. One area deals with synthetic method development and production of health-beneficial oligosaccharide structures. The other area is dedicated
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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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elements for in-materia computing, as part of a joint theoretical/experimental program carried out at AMOLF (Amsterdam) and TU/e (Eindhoven). Computing with artificial neurons takes inspiration from nature’s
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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