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-throughput laboratory systems Proficiency in Python or similar programming languages for data processing, statistical analysis, and integration with AI/ML tools Excellent written and verbal communication
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, physics, computer science, and/or data science Demonstrated accomplishments in materials informatics, scientific machine learning, or AI-guided experimental design Strong Python and scientific computing
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relevant for data manipulation and analysis including experience with creating and using complex models in Simulink & scripting in Matlab, Python, R. A successful candidate must model Argonne’s Core Values
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environment. Demonstrated strong analytical and problem-solving skills. Experience in studying geological or biological systems, and tomography are beneficial. Programming expertise in Python or other
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engineering, or a related field. Strong programming skills in Python and experience developing research or production-quality machine learning software. Experience with machine learning or deep learning
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., Python, Fortran, C++) Knowledge of data analysis techniques and statistical methods Proven scientific writing and oral communication skills Ability to work both independently and collaboratively in a team
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sensing, quantum information science, superconducting circuit, or magnonics. Proficiency in scientific software development (e.g., Python, COMSOL, HFSS or similar languages). Ability to model Argonne’s core
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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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acquisition, instrument control, and data analysis in Python or similar languages Knowledge of quantum optics measurements such as photon correlation functions or photon-number-resolved detection Familiarity
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& Pressure Vessel Code, API standards, R5, RCC-MRx, or similar documents. Coding experience in Python. Skilled in oral and written communications, with the ability to present research at all levels