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Manufacturing and Advanced Materials, Energy Storage and Transportation, Radiopharmaceutical Therapies, and Circular Bioeconomy Systems. To learn more about UT-ORII visit utorii.com . More About UTIA The UT
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experimental design. Collaborate with another postdoc in the NIH Center to use scientific machine learning (SciML) to automatically select mathematical models from data. Minimum Requirements: Ph.D. in applied
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drug overdoses. The translation of cutting-edge data science methods and results into usable scientific, policy-related or clinical knowledge is a particular focus. Example learning problems include
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, Resnick Campus in the Bronx, the Gruss Institute in Jerusalem, the Boys High School in Manhattan and the Girls High School in Queens. From the distinguished faculty who teach here, to the dedicated staff
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to design and implement machine learning techniques and algorithms. * Demonstrated expertise in the Linux computing environment. * Excellent planning, organization and execution skills. * Positive attitude
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. Knowledge on multiphase (gas-particle two phase system), thermal energy storage, and/or renewable hydrogen technologies. Familiar with application of machine learning and deep learning algorithms to fluid and
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) Developing machine-learning based exoskeleton controllers to work across tasks 2) Designing and validating new robotic lower-limb prostheses 3) Exploring other high-risk high-reward research areas related
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and cell culture techniques are desired. The candidate is expected to work closely with an interdisciplinary research team and must be motivated to acquire new experimental skills. PROJECT DESCRIPTION
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Research Assistant Professor, Research Associate Professor, Assistant Professor, Associate Professor
Toxicology and other departments and centers at the Brody School of Medicine and East Carolina University. The tenure-track faculty member will also be expected to teach pharmacology and/or toxicology
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informatics, biomedical engineering, statistics, or related fields. The lab is engaged in developing novel deep learning and AI-based technologies for digital biopsies from medical images and real-world