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] Subject Areas: Computational Biology / Data Analytics Analytical Chemistry / Current Advances in Chemistry & Biochemistry Machine Learning / Machine Learning Computational Science and Engineering
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. A major focus will be on the identification of small molecules from mass spectrometry-based metabolomics data, in part based on generative AI models of chemical structures. The position is available
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maintaining a shock tube facility (operational proficiency required)Kinetic modeling proficiency (Chemkin, Cantera), analytical proficiency (sensitivity, rate of production, etc.)Spectroscopic modeling
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required) Kinetic modeling proficiency (Chemkin, Cantera), analytical proficiency (sensitivity, rate of production, etc.) Spectroscopic modeling experience preferred (HITRAN/HITEMP) Familiarity with
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Research Associates. DDSS supports technical and methodological innovation in quantitative and computational social science, addressing a diverse array of new data and analytic challenges, facilitating
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laboratory plasmas. The applicant is required to have a PhD in physics, or a related field, and have a strong background in computational astrophysics. The expected starting date is negotiable. Appointments
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quantitative and computational social science, addressing a diverse array of new data and analytic challenges, facilitating impactful multidisciplinary collaboration, scholarly advancement, and the creation
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on projects related to machine-learning for mass spectrometry-based metabolomics data. Positions are available starting July 2024, and will remain open until excellent fits are found. Successful candidates will
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to conduct numerical studies for non-local flow-driven magnetized plasma instabilities and their associated nonlinear transport in astrophysical and laboratory plasmas. The applicant is required to have a PhD