43 programming-"the"-"DAAD"-"U"-"Prof"-"IMPRS-ML"-"UCL"-"Inserm" Postdoctoral positions at Argonne
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may include work at Jefferson Lab, the Electron-Ion Collider (EIC) program, detector research and development, and applications of AI in nuclear physics. Applications received by Tuesday, November 4
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
Knowledge in modeling and algorithms for large-scale ordinary differential equations (ODEs) and differential-algebraic equations (DAEs) Proficiency in a scientific programming language (e.g., C, C++, Fortran
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Extensive knowledge of Microsoft Excel and good computer programming skills Knowledge of techno-economic analysis and life cycle analysis Experience working with Argonne’s EverBatt model, GREET model, and
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the domains of environmental, water, and energy system analysis. Prepares reports, papers, and presentations for conferences, workshops, and technical journals. Supports program development including
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physics, etc. Proficiency in Python or other scientific programming languages. Programming skills in numerical methods for image processing and AI/ML methods for quality improvement are advantageous
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and program managers. Position Requirements Minimum Education / Experience Requirements: A Ph.D. in physics, applied physics, electrical engineering, or related field. Additional Requirements: Normal
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of multi-omic data Programming Proficiency: Strong knowledge of Python, C/C++, Julia, and other relevant programming languages Ability to model Argonne's core values of impact, safety, respect, integrity
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with applying unsupervised ML algorithms such as autoencoders, clustering, to time-series data is preferred Experience with the data from HEP experiments is strongly required Programming expertise in
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four staff members [Ian Cloët, Alessandro Lovato, Anna McCoy, and Yong Zhao] and several postdocs and students. The group has a broad research program in QCD/hadron physics and nuclear structure
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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