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allow users to input CDR forcing (e.g., alkalinity addition) and produce day-by-day forecasts of CO2 uptake and storage durability. The project combines physics-based modeling, machine learning, and high
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on topics including mathematical foundations of data science and statistical/machine learning, with an emphasis on inverse problems and in-context learning for PDEs and interacting particle systems
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collaborates with faculty, students, and external partners to translate rigorous methods into practical tools. QUALIFICATIONS: Ph.D. in Applied Mathematics, Electrical & Computer Engineering, Computer
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: This project is related to machine learning for Urban Informatics. In this context, the intersection between the urban infrastructure and digital technologies plays an essential role. The aim is to develop
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developing new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health
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About the Position We are recruiting a Postdoctoral Research Associate to join Tom Hartvigsen’s research group in UVA’s School of Data Science. The postdoc will work on developing machine learning
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, machine learning and surrogate modeling; and presentation skills and proven research independence. Application material should include a cover letter, CV, and the names and contact information of three
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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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on power system dynamics and control under high inverter-based resources (IBRs). * Develop and apply artificial intelligence (AI)/machine learning (ML) techniques for power system planning, operation
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: Support the development of AI and machine learning algorithms for autonomous navigation. Assist in building digital twin models to monitor drone health and mission performance. Contribute to IoT integration