408 machine-learning-"https:" "https:" "https:" "https:" "https:" Postdoctoral research jobs in United States
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that are critical to creating effective learning and work systems within engineering. The postdoctoral scholar will collaborate with Professor Alexandra Coso Strong to examine how institutions build capacity (e.g
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visitors from all over the globe who come to learn, study, teach, and discover. FHL is committed to fostering an environment that is professional, ethical, inclusive and respectful of all who participate in
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/tools or machine learning algorithms. Good computer programming skills in R/Matlab/PerlPython. Knowledge of basic molecular biology, genomics, and epigenetics. Experience in next-generation sequencing
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hardware Experience with atomic layer deposition and process development Experience with thin film and materials characterization Strong background in computational materials science and machine learning
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 2 months ago
, theoretical statistics, or related fields. Applicants should have a solid background in probability and statistics/machine learning. The postdoctoral fellow will be mentored by Alexander Rakhlin (MIT). We will
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/Certification N/A Physical Requirements Sitting for long periods of time, repetitive motion related to computer work, physical activity related to lab and fieldwork; including regular standing, walking
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science tools, such as machine learning methods, causal inference, particularly in economic and social science applications. Prior experience working with Large Language Models is a plus. Experience working
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of, or practical experience with, program evaluation. Preference will also be considered for candidates with experience and/or training in using predictive analytics and machine learning techniques
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Posting Summary Logo Posting Number RTF00237PO25 USC Market Title Post Doctoral Fellow Link to USC Market Title https://uscjobs.sc.edu/titles/156385 Business Title (Internal Title) Post Doctoral
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work closely with I-CREWS researchers across the state. The position emphasizes developing, integrating, and applying modeling approaches—including machine learning (ML), hydrologic and energy systems