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microscopy. • Expertise in metabolomics with mass spectrometry is desired. • Strong general computer skills, experience with databases and scientific applications, and ability to quickly learn and master
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of reactive force field molecular simulations, supervised machine learning techniques and understanding of mass spectrometry techniques. The post is available for 3 years from 1 September 2026. If you are still
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and machine learning with density functional theory, or other similarly relevant computational methods, to advance understanding of materials design predictions for 2D and 3D systems with electronic and
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-driven approach for optimizing the growth of semiconductor materials by combining machine learning with a physics-based understanding of the growth process. Doping and processing of ultra-wide bandgap
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mechanics, machine learning or applied/computational mathematics. ● Demonstrated ability to carry out original mathematical derivations and/or develop computational tools, evidenced by publications
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technologies. The candidate will work at the intersection of Artificial Intelligence, Scientific Machine Learning, and Energy Materials, contributing to the design of self-driving laboratories and no-code
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The Applied Mathematics and Computational Research Division at Lawrence Berkeley National Laboratory (Berkeley Lab) is seeking a Postdoctoral Researcher – Scientific Machine Learning & Computational
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. Knowledge of survival analysis. Knowledge of machine learning methods. Other Requirements Application materials must include: Curriculum Vitae (CV) Statement of research interests Names of three professional
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or quantitative methods. We are open to a range of methodological backgrounds, including digital trace data analysis, natural language processing, machine learning, experimental design, causal inference, and
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. Successful candidates may come from any relevant discipline. All requirements for the PhD or other terminal degree in the relevant field must be completed by August 1, 2027. A record of publication and