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/C++, FORTRAN and/or Python. Experience working with geo-spatial information, remote sensing data, and GIS software. Experience in deep learning and computer vision. Experience in developing software
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the JAX Python library, including efficient implementations of classical numerical algorithms. 2. Extend the hybrid FEA-ML framework to include nonlinear cohesive zone models with simple traction separation
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Neuromorphic computing or Spiking Neural Networks Machine learning, AI/ML algorithms, or scientific computing Strong programming skills in Python and/or C++. Preferred Qualifications: Ability to work
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● Strong background in observational or computational cosmology, large-scale structure, weak lensing or image processing ● Proven experience in scientific programming in Python and/or C++ ● Deep familiarity
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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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, policymakers, and advocates. Programming skills are essential (we mostly work in Python, though other languages are welcome), as is a strong grasp of statistics and an eagerness to learn more. Recommended
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interpret data, will publish results, will represent the university at conferences and meetings, and may develop new theories and methodologies. Extensive professional experience using R, Python or similar
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with data center modeling is a plus. Hands-on experience with new IBR models (e.g., WECC REGFMA1, REGFMB1, vendor-provided models) is a plus. Experience with software development in Python, C++, or other
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. LICENSES, TOOLS, AND EQUIPMENT: Personal computers and printers; word processing, database management, spreadsheet software, other scientific analysis software (R, python, MatLab), laboratory equipment as
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Python and either PyTorch or TensorFlow is required Experience using High-Performance Computers (HPCs) is preferred Ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork