77 computational-physics-"https:"-"https:"-"https:"-"https:"-"UCL" Postdoctoral positions at Argonne
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samples and characterize dynamic behaviors. The candidate will be part of a highly collaborative team and actively interact with other groups, including optics, computation, and time-resolved research
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The Mathematics and Computer Science Division (MCS) at Argonne National Laboratory is seeking a Postdoctoral Appointee to conduct cutting-edge research in scientific machine learning, focusing
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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
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. The researcher will develop and apply physical, chemical, and electrochemical models for advanced battery technologies and associated manufacturing processes. This work will quantify and explain relationships
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-the-loop exploration of extreme-scale scientific data. This position sits at the intersection of scientific visualization, agentic AI systems, human–computer interaction (HCI), and high-performance computing
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it in Physics with a focus on accelerator physics, or Electrical Engineering with a focus on RF/Accelerator Physics Strong background in accelerator physics and beam diagnostics. Excellent problem
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programming, interfacing hardware, and developing machine-learning methods highly desirable. The researcher will join an Argonne funded project with interdisciplinary team of material scientists, computer
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methodologies and tools for economic and ecological analyses of hydropower systems. The position will involve the development and use of computer models, simulations, algorithms, databases, economic models, and
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in materials for electrochemistry. While the focus in on computational expertise, this position will involve some experimental work in adapting workflows for automation and artificial intelligence
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PhD (within the last 0-5 years) in field of physics, chemistry, materials science, electrical engineering, or a related field Demonstrated expertise in electronic structure theory Experience with large