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linear programming, mixed-integer programming, nonlinear optimization, stochastic optimization, robust optimization, or multi-objective optimization. Experience integrating machine learning or data-driven
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programming and data analysis (Python, MATLAB, or equivalent). Experience with machine learning, data-driven modeling, or AI methods applied to physical sciences datasets. Familiarity with handling and
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-completed PhD (within the last 0-5 years) in field of physics, engineering, or a closely related field Demonstrated programming proficiency in C/C++, Python, or another scientific programming language
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is supported by a DOE-funded research program on ultrafast science involving Argonne National Laboratory, University of Washington, and MIT. The goal of this research program is to understand and
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linear, mixed-integer, and stochastic programming. Work with programming languages such as Python, Julia, or C++ to build robust analytical tools and perform large-scale data analysis. Collaborate with
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, engineering, or related field Comprehensive experience programming in one or more programming languages such as Python, C/C++ Experience with at least of one of the AI frameworks is required, such as PyTorch
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workflows. 3. Programming Skills: - Proficiency in C++/or Python programming languages is essential. 4. Research Contributions: - Demonstrated publications in AI for Materials Chemistry. 5. Collaboration and
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deep learning including data collection, architecture development, model training, and validation Interest in software development, with particular emphasis on the Python programming language and
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Interaction, or a related field Strong background in scientific visualization, visual analytics, or interactive data exploration Experience programming in one or more languages such as Python, C/C
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) or equivalent experience in a computational science discipline, computer science, or in a related field Strong programming skills in one or more scientific programming language, such as C++ and Python Experience