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oxides, hydroxides and hydrides using a combination of solid-state density-functional theory (DFT) and machine-learning force fields (MLFFs). DFT methods will be used to study materials of interest
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gas turbine sensor data, if available, will be utilized to validate the developed digital twin in order to estimate non-measurable health parameters of major gas path components, including compressors
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contribute to the development of next-generation methodologies and theories for customer experience management. Collaborating with CSA industrial partners and interdisciplinary teams, they will lead the design
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will be augmented with atomistic structure data from electronic structure theory and STEM image simulations. All data will be combined into an automated workflow that predicts thermodynamically stable
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together knowledge in fields as diverse as, e.g., mathematical logic, the theory of computation, software engineering, artificial intelligence, data science, global and local history, classical philosophy
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those of ttW, ttZ and more which all share a similar final state. The candidate will also be involved in such efforts including searching for new physics via effective field theory (EFT) techniques
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applications such as energy storage, solar, and carbon capture. The project will explore methods beyond traditional density-functional theory (DFT), leveraging cutting-edge techniques such in machine learning
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aforementioned tasks with the following actions: Develop the principles and theories for governing the scalability principles for building innovative robotics end-effectors that can access geometrically complex
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art-historical, historical and cultural theories revolving around early modern trade and colonialism. In terms of public impact, the student will have the opportunity to contribute to the redevelopment
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zone in a very complex manner and lead the modelling to an imperfect zone of assumptions. These complexities allow the researchers to use approximations for useful lifetime calculations. Based