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with computer-aided design software. Knowledge of deep machine learning (using TensorFlow, PyTorch, etc.) for multi-fidelity modeling, regression tasks, management and analysis of large datasets, and
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data analysis implemented in languages such as python or Matlab Desirable: Experience with dilution refrigerators and closed-cycle cryogenic systems. Experience with microwave engineering and various
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modeling of crystals, dislocation dynamics, and defect analysis, linking atomic-scale simulations to macroscopic properties. Familiarity or interest in machine learning methods and computing frameworks
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equipped with world-leading full-field imaging instruments, including ultrahigh-speed imaging. The group also develops end-to-end scientific software, data analysis, and interpretation methods
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or upcoming year (optional) Experience in one or more of the following areas: experimental data analysis related to hadronic physics, polarized targets or beams, silicon sensors, calorimetry, detector
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in soil processes, plant physiology, plant traits, and species composition Strong data analysis skills, including proficiency in R or Python coding, and/or machine learning techniques Excellent writing
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research projects, data analysis, physics interpretations, and reporting of the results. A strong background in the field of Experimental Nuclear Physics. Ability to model Argonne’s core values of impact
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, the ALCF is studying the application of these techniques to a variety of our science applications, including but not limited to: Computational Chemistry, Plasma Physics, High Energy Physics, analysis
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cosmology, and simulations. Analysis efforts cover topics such as CMB power spectra, CMB lensing, galaxy clustering, redshift-space distortions, and weak and strong gravitational lensing. The observational
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) Demonstrated knowledge of and experience in general analytical methods and practices Experience in programming for data analysis Excellent oral and written communication skills Demonstrated creative and