77 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"U.S" positions at Argonne
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experiments and corresponding data analysis. Following the successful demonstration of the technique, the candidate will collaborate with team members from material science to apply these methods to scientific
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related field Strong molecular biology skills (cloning, vector design, transformation), protein and nucleic acid prep-scale purification and analysis, and quantitative data analysis Excellent communication
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experimentalists, modelers, and data scientists Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in field of Materials Science, Chemical Engineering, Chemistry, or a related field
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capable of hypothesis generation, experimental design, data analysis, and iterative learning in biological contexts. This project seeks to transform how computational and experimental biology research is
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. Design, implement, and validate experimental setups; conduct synchrotron-based measurements on quantum and energy materials. Build robust data reduction and PDF analysis workflows; document best practices
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programming, interfacing hardware, and developing machine-learning methods highly desirable. The researcher will join an Argonne funded project with interdisciplinary team of material scientists, computer
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Hands-on experience with two-dimensional materials modeling Proficiency in database development and management for computational materials data Strong programming skills and experience with software
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time The expected hiring range for this position is $70,758.00-$117,925.00. Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be
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techniques, and carry out any required data analysis. Position Requirements • Experience working with Lorentz Transmission Electron Microscopy. • Strong background in Materials Science or Physics. • Min
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preferred. Strong data analysis skills and ability in programming languages, such as Python, for performing experiments and analyzing data. Knowledge of innovative phase retrieval algorithms is desirable