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associate in the broad areas of high performance computing and machine learning. HighZ is focused on developing scalable high order methods, enhanced with surrogate models for subscale physics, for modeling
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machine learning methods for computational materials physics and chemistry. Projects include: The aim is to develop generalized equivariant neural network models NequIP and Allegro for machine learned
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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. Our faculty and staff are diverse and gender balanced
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methods and data analytic strategies and their applications. The Magnetom Cima.X offers an unprecedented opportunity to be at the forefront of establishing an outstanding research program in multimodal