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microelectronics project. To learn more: Argonne to lead two microelectronics research projects under U.S. Department of Energy initiative | Argonne National Laboratory Position Requirements Recent or soon-to-be
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cells and electrolyzers is welcomed. Experience with statistical analysis methods such as PLS-DA, supervised learning and database building are highly encouraged. The applicant is expected to think and
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to analytical techniques for characterizing electrolytes using UV-VIs absorption spectroscopy, ICP-MS, LC-MS, GC-MS, and ICP-MS. This position will include learning experimental workflows and adapting them
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-of-the-art data management, machine learning and statistics techniques. With the advancement of Exascale systems and the variety of novel AI hardware designed to accelerate both training and inference
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. - Experience with automation and computer interfacing - Experience with advanced data analysis implemented in languages such as python - Direct research experience in quantum material systems used for quantum
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
: Expertise in rare event simulation, deep learning, and developing computationally efficient approaches for simulation and modeling in complex systems is highly desirable Experience with parallel computing
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The Data Science and Learning Division (DSL) at Argonne National Laboratory is seeking a postdoctoral researcher to conduct cutting edge molecular and microbiology work to enhance non-proliferation
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performing experiments to acquire data, using and maintaining research equipment and instruments, compiling, evaluating and reporting test results. Knowledge and experience in chemical thermodynamics, kinetics
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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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skills and familiarity with LLM APIs (e.g., OpenAI API), agent frameworks (e.g. LangChain), PyTorch, and the Python scientific stack (e.g., numpy, pandas, scikit-learn). Experience with front-end