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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 10 hours ago
, Competencies, and Experience Strong research background in computer vision and multimodal machine learning. Experience with deep learning and multimodal/vision-language models. Strong programming and research
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world and a suite of associated machine learning tools. The incumbent will be advised by Dr. Laurel Symes (CAPS, [email protected]). Depending on the research direction, collaboration and additional
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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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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 7 days ago
); and propagation of physics-traceable soil moisture uncertainty into precipitation estimates. Machine learning approaches are welcome as tools within this framework — for example, learning the space–time
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– Wrocław University of Science and Technology offers employees the opportunity to teach students and participate in training programmes. International cooperation – Wrocław University of Science and
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University of California, Los Angeles | Los Angeles, California | United States | about 18 hours ago
seeking applicants for a full-time postdoctoral scholar position in Computational Immunology and Genomics. The successful candidate will develop and apply computational, statistical, and machine-learning
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to industry and developments with Deep Learning (DL), Computer Vision (CV), Transformers, Large Language Models (LLMs), Natural Language Processing (NLP). Mandatory requirements • Bachelor's degree
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, please visit: http://uhr.rutgers.edu/benefits/benefits-overview . Posting Summary Rutgers, The State University of New Jersey, is seeking a highly qualified candidate for a Postdoctoral Research Associate
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avenues of research rather than a required work plan. They may be pursued individually or in combination, and we welcome other creative and strategic approaches. Statistical or machine-learning approaches
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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https