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. Requirements: PhD completed less than 7 years ago in Computer Science or related areas; experience in machine learning and data science (supervised/unsupervised models, recommendation and evaluation/robustness
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Postdoctoral Research Associate will be responsible for AI-driven materials discovery. The position will collaborate with interdisciplinary teams to develop integrated molecular simulation and machine learning
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Computing Applications group in CSD has an immediate opening for a Postdoctoral Research Associate to design, develop, and deploy machine-learning and high-performance computing workflows, algorithms, and
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MRIs (available in HIE cases) and radiomic features predictive of school-age MRI extracted. Machine learning algorithms of radiomic features predictive of future brain development will be developed
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for this position will work as a member of an interdisciplinary team led by Dr. Colin Xu on Department of Defense (DoD)-funded research project involving the use of statistical and machine learning methods
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states, charge density waves, superconductivity, and quantum magnetism - Kagome materials and superconducting hydrides - Machine learning interatomic potentials (MLIPs) and data-driven atomistic
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discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials. Candidates who are nearing completion
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Python and R; - Demonstrable experience with Machine Learning; - Excellent problem-solving skills and the ability to work both independently and as part of a team. This position is for full-time, on-site
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semimetals, magnetic monopole fluids, quantum spin liquids and exciton fluids. • Data Management, Analysis & Machine Learning Knowledge and experience in high volume image-array data acquisition and management
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development spanning areas such as optimization, Fourier analysis, numerical linear algebra, statistics, machine learning, and high-performance computing for one or more of the following: (1) reconstruction