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Field
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for the EAP and Cornell community Participate in EAP and Center for International Studies initiatives that relate to the scholar’s work or interests No teaching required. However, if the scholar wishes to teach
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calculations Materials modeling/electronic structure calculations Machine Learning/Deep Learning techniques. Education and Experience: A PhD in physics, astronomy, or a closely related field must be completed
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(PhD, MD, or equivalent) conferred by the start date. Proven research and/or professional experience in machine learning and/or natural language processing, with a preference for prior experience working
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multidisciplinary team comprised of fellow postdoctoral appointees, experimentalists, and staff scientists, with computational fluid dynamics (CFD) and artificial intelligence/machine learning (AI/ML) expertise, with
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position to develop and apply advanced analysis methods, including artificial intelligence and machine learning algorithms and approaches, for x-ray science and instruments. These methods will accelerate
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Bioinformatics and/or Cheminformatics. Experience working in computational drug discovery, particularly multiscale applications. Experience working in Python and Bash. Understanding of machine/deep learning topics
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theory, multi-objective optimization and machine learning. The specific project aims to understand the multiscale interactions shaping human gut bacteria and human gut pathogens. The project will combine
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workers, staff, and faculty to teach and support these workshops. 3. Assist with the development and support of funded DH scholarship, both at USM and at partner institutions across the state, including
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management machine learning, distributed computing, and resource optimization leveraging the unique computational resources available at ORNL, including the Frontier supercomputer—the world's first exascale
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Work Experience M.D. or PhD Required Qualifications A qualified candidate must hold a doctoral degree in biology or biomedical sciences. A combination of skills in cell culture, animal disease models