72 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Argonne
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to state-of-the-art research facilities and gain in-depth knowledge of the research frontiers of in situ characterization of deposition science and electrochemical interfaces. Position Requirements: A PhD in
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in high-voltage battery systems through a fundamental understanding of interfacial mechanisms. Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in Organic Chemistry
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performing experiments to acquire data, using and maintaining research equipment, compiling, evaluating, and reporting test results. Problem-solving skills, including the ability to identify technical
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or equivalent. Knowledge and experience with analytical techniques such as XRD and SEM. Skill in devising and performing experiments to acquire data, using and maintaining research equipment, compiling
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information science and light–matter engineering, while engaging with CNM’s cleanroom and characterization capabilities, APS ultrafast and nanoprobe X-ray beamlines, MSD’s THz initiatives, and Q-NEXT’s national quantum
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, and safe laboratory practices. Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in the field of Chemistry or a closely related discipline Demonstrated expertise in
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electrochemical methods such as cyclic voltammetry and electrochemical impedance spectroscopy is desired, but not required. · Experience working directly or collaboratively with computational methodologies
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scientists with extensive microelectronics (materials and devices), AI, computational materials science and materials characterization expertise; and will be expected to bring the electrochemical expertise
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the domains of environmental, water, and energy system analysis. Prepares reports, papers, and presentations for conferences, workshops, and technical journals. Supports program development including
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. Preferred Knowledge, Skills, and Experience Prior experience with high-throughput or computational protein design/screening techniques. Background in structural biology (CryoEM/crystallography) Knowledge