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science, or a closely related field. Experience with the finite element method. Proficient in C++ and Python. Skilled in Unix-based operating systems. Skilled in oral and written communications and presentations
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, engineering, computational science, a physical science (materials science, chemistry, physics etc.), or related field. Hands-on experience with AI frameworks and employing large language models. Strong Python
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Science, Chemistry, Chemical Engineering, Electrical Engineering, Computer Science, Physics, or a related field Demonstrated proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow
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areas: high-pressure science, synchrotron X-ray techniques, rheology, Raman spectroscopy, and Python programming, among others. Ability to model Argonne’s core values of impact, safety, respect, integrity
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applying machine learning or other elements of artificial intelligence to solving significant scientific or engineering problems Interest in software development, with particular emphasis on the Python
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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
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completed in a relevant field of study. Experience with deep learning (DL) frameworks such as PyTorch, TensorFlow, or JAX. Strong programming proficiency in Python. Demonstrated experience with coherent X-ray
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programming languages such as Python, Go etc. Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork. Interpersonal skills, oral and written communication skills, and ability
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instrumentation and experiments. Proficiency in scientific programming and data analysis (Python preferred; experience with NumPy/SciPy, Jupyter, version control). Experience with C/C++ or MATLAB is a plus
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devices, nonlinear optics, or microwave photonics Working knowledge of simulation tools such as COMSOL or Lumerical for electro-optic modeling Desirable Skills Proficiency in Python-based data analysis