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process. An integral part of the project will be the development of enhanced data-driven physics methods to achieve reliable prediction of material removal rate and material removal distribution
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. Develop and apply state-of-the-art electron microscopy methods to study molecules-adsorbents interfaces. Collaborate closely with TUM to correlate nanoscale insights with material performance. Contribute
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the project will be the development of physics enhanced data driven methods to achieve reliable prediction of residual usable life of milling tools. The approach will be validated by application to industrial
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algorithm. Design methods: Develop novel control methods for power electronic converters feeding electric machine Simulation: Learn advanced simulation tools such as Ansys to simulate and analyze the effect
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Public Health, Sociology, Medicine or similar disciplines. We prefer a candidate with the following qualifications: Strong background in epidemiological methods, health policy, or healthcare systems
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research in the field of modern high voltage polymer electrolytic capacitors, develop models for lifetime prediction, methods to predict and test for reliability, understand physics of failures at elevated
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Small case Studies of Successful Innovation Funding Methods The project will employ a combination of mainly quantitative methods, including machine learning (ML) and generative AI (GenAI) models
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social science methods. Strong writing skills. Further information For further information about the position, please contact Associate Professor Tonny Brems Knudsen, Department of Political Science
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of programming languages. The ideal candidate has an MSc in Computer Science or Mathematics and experience in one or more of the following areas: Theory of programming languages. Logical methods in
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. Responsibilities: Conduct research in time-predictable computer architecture. Designing a network-on-chip for real-time automotive systems Verify the design with modern verification methods, such as function