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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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the past 2 years [4]. The current industrial standard photovoltaic (PV) module design with difficulty in recycling has generated worldwide concerns regarding an estimated stream of 212 million tons of end
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of the proposed methods. Identifying the critical assumptions needed to draw inferences from empirical results. Writing computer code to analyse experimental or secondary data according the best practices and tools
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has been studying any of these topics: statistical physics, computer simulation methods, and polymer physics. Proficiency in the C++ and/or Python programming language is an advantage. Good knowledge
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Strategy. The ability to upgrade methane into higher-value products, particularly through plasma-assisted or catalytic methods, is a game-changer for the chemical industries, enabling a dual focus on
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methods, is a game-changer for the chemical industries, enabling a dual focus on reducing greenhouse gas emissions and creating economically valuable outputs. This PhD position is part of PRIME LEAP, a
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types of disabilities further complicate this issue, as each group may face unique obstacles during wayfinding and navigation. There is an urgent need for innovative approaches and methods to design and
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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
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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