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Lecturer – PHY1610HS – Scientific Computing for Physicists Course description: Scientific Computing is a graduate course on research computing, covering techniques and methods for reliable and efficient
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, engineering, materials science, maths, or computer science), or equivalent experience Experience with uncertainty quantification or error analysis Familiarity with numerical methods (e.g., Monte Carlo, Finite
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independent and collaborative research in theoretical quantum materials. Develop and apply a combination of analytical and state-of-the-art numerical methods to address fundamental problems in condensed matter
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system transient simulation concepts. Advanced mathematical knowledge in linear algebra and calculus. Expertise in numerical methods for solving large-scale power system equations. Programming proficiency
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; David Plant ECSE 335: Microelectronics; Gordon Roberts ECSE 343: Numerical Methods in Eng; Roni Khazaka ECSE 353: Electromagnetic Fields & Waves; Thomas Szkopek ECSE 354: Electromagnetic Wave Propagation
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degree in computer science and: You have a good knowledge of C++ You have skills in software engineering. You are familiar with common development environments and associated tools Knowledge of parallel
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on methods development in machine learning, uncertainty quantification and high performance computing with context of applications from the natural sciences, engineering and beyond. It is embedded in
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groups, and a laboratory for research and innovation in the application of advanced computational and data intensive research methods, working in partnership with academics from all fields. We are a home
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learning Demonstrated expertise in software and algorithm development, computational methods, data analysis, modeling, machine learning, high-performance and parallel computing, or scientific simulation
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Martian meteorite falls using advanced correlative microscopy techniques. To determine if they are the same or different Methods We will use a correlative, big data approach that combines X-ray computed