85 computational-physics-"https:"-"https:"-"https:"-"https:"-"IFM" positions at Argonne
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bioreactors; collect, analyze, and interpret biological, chemical, and microbial omics data; and integrate results to evaluate process performance and scalability. The candidate will contribute to process
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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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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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-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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. 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 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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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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, instrumentation, modeling, and data science Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in field(s) of materials science, physics, computational science, or a related field
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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