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Field
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Your Job: Digital methods for inverse materials design are essential to efficiently create new, sustainable and recycling-adapted structural metals. Alloys with a reduced number of elements, so
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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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part of an interdisciplinary team which works on cutting-edge questions ranging from mathematics and theoretical physics all the way to numerical simulation algorithms? Then apply now to join our team
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biophysics. Familiarity with simulation environments, numerical methods, or machine learning approaches is an advantage. Fluent command of written and spoken English is necessary; German is an advantage but
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physics all the way to numerical simulation algorithms? Then apply now to join our team of researchers in the Quantum Information and Quantum Many-Body Physics research group. Your personal sphere
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Group in the Social Networks Lab at ETH Zürich is developing network data science methods and applying them in a variety of domains. Our current research focus is on sports analytics and we seek to expand
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glove boxes and Schlenk methods Experience in X-ray diffraction techniques and other structural characterizations Experience in working with radionuclides, esp. actinides and TRU elements is of advantage
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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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understand, explain and advance society and environment we live in. Your role Conduct research and prepare a doctoral thesis in metabolic network modelling and computational epigenomics Develop novel methods
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an interdisciplinary team which works on cutting-edge questions ranging from mathematics and theoretical physics all the way to numerical simulation algorithms? Then apply now to join our team of researchers in