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-efficient machine learning framework that leverages graph grammar to inverse-design polymers with tailored thermal and mechanical properties. By interpreting molecules as graph networks, they will train a
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decision support data or analytical capabilities. * Generating computationally numerical algorithms for data processing and analysis, using supervised and unsupervised machine learning models and methods
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and often different from the canonical types of data used to benchmark machine learning (ML) algorithms. In this opportunity, we will be evaluating how state-of-the-art ML techniques can be used
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This research involves conducting fundamental and applied research in the field of Robotics and Autonomous Systems (RAS) and Artificial Intelligence/Machine Learning (AI/ML). The fellowship position is based
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educational and professional goals. As the selected participant, you will learn about public health, environmental health risk assessment, military deployment health, and/or environmental exposure assessment
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researchers in an effort to investigate and analyze program productivity. Why should I apply? Under the guidance of a mentor, you will engage in a variety of research activities, including: Learning
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of California Irvine students have opportunities to learn and apply their knowledge of industrial efficiency, waste stream reduction, and advanced manufacturing. IAC students will have access to specialized
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offices. Learn More: Learn more about NNSA-MSIIP: https://orise.orau.gov/NNSA-MSIIP/ Contact us: NNSA-MSIIP@orise.orau.gov . Connect with ORISE...on the GO! Download the new ORISE GO mobile app in
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, improved, and further miniaturized. The learning objectives for this project are: (1) learning about the design and operation of an advanced laser induced breakdown spectroscopy system for downhole and harsh
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postdoctoral fellow interested in learning and using contemporary functional genomics approaches (CRISPR, RNAi, transgenics, etc.) to characterize the functions of genes involved in biological development