86 affective-computing-"https:"-"https:"-"https:"-"UCL"-"UCL" Postdoctoral positions at Argonne
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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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computational research in accelerator science and technology. The focus is on developing and applying machine learning (ML) methods for accelerator operations and beam-dynamics optimization in advanced
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This is an opportunity for a knowledgeable and creative individual to be part of a team using artificial intelligence and high-performance computing to evaluate the state of health (SOH
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on understanding novel and emergent behavior in nanoscale magnetic heterostructures, particularly in confined 2D van der Waals magnets and related devices. The goal of the program is to study and control magnetic
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analysis Interdisciplinary Collaboration - Experience working in cross functional teams including molecular biologists, chemists, radiation experts and computational biologists Core Values - Ability to model
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contribute to open-source code repositories and documentation. Position Requirements Required skills, knowledge and qualifications: PhD in physical oceanography, coastal engineering, computational science
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facilities and instrumentation, publish in high-impact venues, and collaborate closely with scientists across CNM, Argonne, and external partners. Key Responsibilities Design, synthesize, and rigorously
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design, advanced modeling and high-performance computing, mathematics and data analytics, AI/ML algorithm development, and accelerator operations Ability to model Argonne’s core values of impact, safety
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, oral and written communication skills, and ability to interact with people at all levels both within and outside the laboratory. Ability to model Argonne’s core values of impact, safety, respect
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experience in economic and supply chain analysis, computational modeling, or policy analysis. Proficiency in scientific programming languages (e.g., Python, R) and data analysis libraries (e.g., pandas, NumPy