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. The ideal candidate should have a strong background in artificial intelligence and machine learning, with demonstrated experience in developing and training neural networks for predictive modeling. Position
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in AI for genomics (e.g., generative models, transformers, genomic language models, agentic AI) and related areas of statistics (e.g., uncertainty quantification for machine learning and AI). Apply
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paired with computational biology and machine learning to develop predictive AI models of how cells interpret and respond to the surrounding extracellular matrix. Required Qualifications: We are looking
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(ORNL) is seeking a highly motivated Postdoctoral Researcher with expertise in artificial intelligence and machine learning (AI/ML), remote sensing, Earth and environmental sciences, and the analysis
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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IOCB Prague (Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences) | Czech | 2 months ago
of novel computational approaches at the intersection of semiempirical quantum chemistry, machine learning potentials, and implicit solvation models. Requirements Experience in computational chemistry
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converter degradation/failure analysis. Brief Description of Duties: The Postdoctoral Associate will support the Principal Investigator, Dr. Fang Luo , in the Department of Electrical and Computer Engineering
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Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how
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. Demonstrated expertise in using machine learning and optimization frameworks in conjunction with FE simulations to assist with component and/or process design is preferred. Excellent written and oral
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National Energy Technology Laboratory (NETL) | Pittsburgh, Pennsylvania | United States | about 19 hours ago
machine learning to a challenging real-world problem; (3) gain knowledge of data needs for the energy industry; and (4) collaborate and communicate with an interdisciplinary team to extend an existing