714 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"UCL" Postdoctoral positions in United States
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Field
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. The preferred candidate will have a strong academic or industrial background in machine learning, trustworthy machine learning and AI, agentic AI, adversarial machine learning, graph-based learning, multi-domain
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Description Our Commitment Texas A&M University is committed to enriching the learning and working environment by promoting a culture that respects all perspectives, talents & lived experiences. Embracing
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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | about 3 hours ago
analysis software packages. Demonstrated success in collaborative environments. Experience with one or several of the following: machine learning, data assimilation, ArcGIS, NASA satellite datasets. Point
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satisfactory performance and availability of funding. Research topics of interest include numerical methods for scientific machine learning and AI, and their applications to various science and engineering
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Senior Postdoctoral Associate Required Qualifications: (as evidenced by an attached resume) Doctoral Degree or foreign equivalent degree. Three years of research experience in machine learning and
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Senior Postdoctoral Associate Required Qualifications: (as evidenced by an attached resume) Doctoral Degree or foreign equivalent degree. Three years of research experience in machine learning and
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business and entrepreneurship, or applied econometrics. • Strong quantitative skills and experience with cutting-edge techniques in data science, econometrics and machine learning, especially in the areas
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on topics including mathematical foundations of data science and statistical/machine learning, with an emphasis on inverse problems and in-context learning for PDEs and interacting particle systems
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research protocols and procedures, including in computational tasks where data visualization, preprocessing, or interpretation can be improved. Devises and deploys custom machine learning approaches where
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background in one or more of the following areas: dynamics analysis of power systems, machine learning, cybersecurity, renewable energy, microgrids, hands-on experiences on hardware-in-the-loop projects