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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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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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School (NPS) Center for Infrastructure Defense (CID) leads research that supports the continued operation of critical military and civilian infrastructure systems in the presence of failure, natural
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consequences to sensitive species. Revisions also include updating values in the existing models that reflect new science and lessons learned over the last 15-years. The toolkit also includes evaluation tools
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partners, along with the databases we create. The geospatial smoke tradeoffs dataset can then be used in project planning. Learning Objectives: The fellow will develop professionally while engaging in
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findings into manuscripts for peer-reviewed journals. Learning Objectives: Throughout the course of this research project, the participant will have the opportunity to explore the latest trends and research
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improve federal transit operations and oversight. Projects may include: Performing exploratory data analysis across diverse FTA datasets. Building and evaluating statistical and machine learning models