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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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The PhD will develop AI methods and approaches to enable accurate characterization of metal scrap, for more efficient metal recovery and recycling Job description The volume of available metal scrap
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and optimizing post-plasma catalysis strategies, we want to create an energy-efficient and scalable method that reduces greenhouse gas emissions and supports the transition to a carbon-neutral chemical
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(material) scales, using a combination of experimental and numerical techniques. Current research is focused on sustainability-related topics, such as alternative materials, ageing and healing response
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of pavements. All these topics are addressed at various (material) scales, using a combination of experimental and numerical techniques. Current research is focused on sustainability-related topics, such as
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written communication skills in English are required. Other advantageous qualities include experience with coding (Python\Matlab) and numerical methods, as well as familiarity with concepts in complex
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essential for transitioning to a carbon-neutral and energy-efficient society. Methane, a key component of natural gas and a by-product of numerous industrial processes such as naphtha cracking, presents a
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by-product of numerous industrial processes such as naphtha cracking, presents a compelling opportunity. Current practices, such as combusting methane to sustain energy-intensive operations
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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