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on developing machine-learning surrogates for electronic structure and electrostatic potential and using these models to predict structural and electronic evolution under applied bias. Methods may include density
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operations is preferred, working knowledge of machine learning and artificial intelligence methods is highly desirable The successful candidate will demonstrate expertise in accelerator physics, accelerator
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science, including electronic structure methods molecular dynamics, and scientific machine learning. Experience with High-Performance Computing (HPC) systems and intelligent workflows. Demonstrated
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Computer Science, Artificial Intelligence, Machine Learning, Computational Biology, Bioinformatics, or a related field. Strong programming skills in Python, with experience in AI/ML frameworks (PyTorch, JAX, Hugging
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independently conducting machine studies to diagnose and resolve operational issues. Support APS performance improvements by conducting accelerator experiments, processing and analyzing data, and performing
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, including both the large-scale production machines and the testbed machines featuring novel architectures such as Cerebras and SambaNova. The list below provides examples of the potential tasks
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computer-aided design software. Collaborative skills, including the ability to work well with other divisions, laboratories, and universities. Ability to demonstrate strong written and oral
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devising and performing experiments to acquire data, using and maintaining research equipment and instruments, compiling, evaluating and reporting test results. Knowledge and experience in chemical
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will require flexibility and a willingness to learn new techniques and approaches. In addition, there may be overnight experiments being run unattended, the candidate must be able to respond to issues in
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. The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne National Laboratories. Primary responsibilities will be