2,430 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" "Univ" Postdoctoral positions in United States
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at the University of Richmond. The postdoctoral researcher will also have the opportunity to teach up to one course per semester in the Data Science and Statistics Program and to travel to international conferences
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Zanna, the successful candidate will focus on developing generative machine learning models for complex dynamical systems for probabilistic forecasts. The postdoc will be expected to lead independent
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to significantly extend our existing team’s capabilities for data scoring and analysis (e.g., with expertise in natural language processing, machine learning, or computational modeling). Finally, the
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mechanics and analysis Experience with the following: Structural health monitoring (SHM) Finite element modeling (e.g., ABAQUS, SAP2000, ANSYS) Machine learning / AI (Python, TensorFlow, PyTorch) Demonstrated
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Prof. Youngseok Kim (Ohio State University) – Quantum Computation & Quantum Algorithms Prof. Pilkyung Moon (NYU Shanghai) – Graphene, Twistronics, Novel Topological Materials Website of our team https
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: Education: Ph.D. in machine learning, computer science, engineering, physical science or related technical discipline. Experience: Expertise in developing and training AI models Proficiency in Python
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, gender expression, gender identity, genetic information, national origin, race, religion, (including pregnancy and pregnancy related conditions), sexual orientation, or military status. Duke aspires
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, gender, gender expression, gender identity, genetic information, national origin, race, religion, (including pregnancy and pregnancy related conditions), sexual orientation, or military status. Duke
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-changing technologies. Life-changing careers. Learn more about Sandia at: https://www.sandia.gov *These benefits vary by job classification. What Your Job Will Be Like: We are seeking a Postdoctoral
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, marital status, military status, national origin, parental status, partnership status, predisposing genetic characteristics, pregnancy, race, religion, reproductive health decision making, sex, sexual