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management, workflow management, High Performance Computing (HPC), machine learning and Artificial Intelligence to enhance our capabilities in making AI-ready scientific data. As a postdoctoral fellow at ORNL
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recording. The work will also include developing new statistical data analysis tools for behavioral and neural data. More broadly, the postdoc will be part of a large and intellectually vibrant community
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The applicant must: hold a PhD in a relevant field (e.g. computer science, artificial intelligence, machine learning, computer vision, animal science, biology, veterinary medicine, or a related discipline) have
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. ESSENTIAL REQUIREMENTS A PhD inMachine Learning, Computer Vision, Computer Science, Physics, Engineering, Mathematics or related areas. Documented expertise in: Machine/Deep Learning, and possibly Computer
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surface-chemistry trends across selected metals and their oxides. These data will support the construction of a machine-learning force field tailored to NHC–surface systems, enabling large-scale molecular
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interdisciplinary fields. For more information of the faculty members, please search the website: https://english.itp.cas.cn/people/faculty/ . The applicant must have obtained a Ph.D. degree in theoretical physics
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, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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teaching and student supervision activities in areas related to your expertise. What we ask of you Your experience and profile A PhD degree in AI (e.g., machine learning, natural language processing
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clustering, redshift-space distortions, weak/strong gravitational lensing, and artificial intelligence/machine learning (AI/ML). The observational focus is on optical sky surveys (DES, DESI, Roman, Rubin Obs
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École nationale des ponts et chaussées | Champs sur Marne, le de France | France | about 2 months ago
of the project, and will contribute to shaping the scientific directions of the AUTOMATIX project. Context The increasing availability of full-field experimental data and advances in machine learning offer new