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plasma. The main methods used are computer simulations for studying dust dynamics and analysing in situ measurements by spacecraft that carry a dust detector on board. We collaborate with researchers in
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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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, 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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competitive residency program with 25 positions, we offer 9 fellowships and participate in numerous graduate school and the MD/PhD program of the CU School of Medicine. Why work for the University? We have
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Knowledge of Matlab, and web-based technologies is of advantage Knowledge in using high-performance compute architectures Experience in implementing and optimizing scientific numeric analysis methods and
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professional values. In addition to a vibrant and highly competitive residency program with 25 positions, we offer 10 fellowships and participate in numerous graduate schools and the MD/PhD program of the CU
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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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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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, which performs numerical analytics during the simulation. This is necessary due to the ever-growing gap between file system bandwidth and compute capacities. To this end, we are developing the Deisa