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The Chair of Non-Destructive Testing (www.zfp.tum.de) is devoted to research and teaching in the field of material characterization using non-destructive testing methods. The focus of the chair includes
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highly motivated candidate to develop models integrating machine learning and domain-specific knowledge to predict failure arising from hydrogen embrittlement. You will carry out materials testing
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mineral and metal-bearing raw materials more efficiently and to recycle them in an environmentally friendly way. The Department of Modelling and Evaluation is looking for a PhD Student (f/m/d) to work in
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integrating machine learning and domain-specific knowledge to predict failure arising from hydrogen embrittlement. You will carry out materials testing, computational model development, data processing, and
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systems using various tools and models, including: i) characterization of the emerging patterns in physical systems (solid state materials and active systems); ii) investigation of the mechanical properties
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of modern physical chemistry, materials science, and biochemistry questions. Our qualification program, measures for recruiting, gender equality and family friendliness are equally innovative and
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that span a broad range of modern physical chemistry, materials science, and biochemistry questions. Our qualification program, measures for recruiting, gender equality and family friendliness are equally
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Your Profile: An excellent master’s degree with strong background in nano science, materials science, chemistry, chemical engineering, physics or related Experience with modeling of reaction kinetics
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theories and numerical methods, carrying out and analysing field and remote sensing observations and conducting and analysing numerical model simulations. The PhD position is funded by the German Research
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Description Are you interested in developing novel scientific machine learning models for a special class of ordinary and differential algebraic equations? We are currently looking for a PhD