743 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" research jobs in United States
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spatial and temporal data analysis using advanced machine learning technologies. The successful candidate will become a part of an interdisciplinary team working to develop machine learning techniques
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 7 days ago
machine learning applied to natural language processing, computer vision, or a related area. Strong publication record in NLP, ML, or related areas -Strong programming skills, including TensorFlow and/or
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, biomedicine, and other areas of societal importance. Coding and/or machine learning experiences are highly valued. Specific projects may involve developing multiscale simulation methods for quantum mechanical
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: Further Info: https://argonne.wd1.myworkdayjobs.com/Argonne_Careers/job/Lemont-IL-USA/Postdoctoral-Research-Associate---Machine-Learning-in-High-Energy-Physics-Detector-Operations_421270 9700 South Cass
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interests in applied statistics, machine learning, or computational biology are encouraged to apply. For more information, please visit our website https://ds.dfci.harvard.edu/postdocs to view the list
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position in biomedical informatics is available at Harvard Medical School to work at the intersection of advanced machine learning and large-scale biomedical data. The selected fellow will join a dynamic
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related field by the start date, with a strong publication record in computer vision, multimodal learning, or vision–language models. We require hands-on expertise with transformer architectures (e.g., ViT
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manufacturing. Student will explore feature dominance, redundancy, and interaction effects using interpretable machine learning techniques. The study emphasizes how data quality, feature selection, and
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knowledge in bioinformatics, machine learning, statistics and programming skills (R, Python, or MATLAB) are required. Record of peer-reviewed publications. Knowledge in one or more of the following areas is
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postdoctoral or more senior researcher interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling