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of Engineering as well as the new Virginia Tech Institute for Advanced Computing (IAC) located in the Greater Washington, D.C. area. The position involves conducting research in signal processing, machine learning
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inverse modeling methods, machine learning, and artificial intelligence techniques. Appointment Details: Start date: As early as February 1, 2026. Annual Salary: $52,000 Application review: Ongoing until
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involve the integration of: Advanced motion planning and control algorithms Multi-modal perception techniques (e.g., vision, tactile, force) Machine learning models for physical behavior prediction and
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automating geospatial workflows. Proficiency with GIS tools (e.g., QGIS, ArcGIS) for spatial analysis and visualization. Experience developing statistical or machine learning models to estimate fuel moisture
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of massive galaxies from the primordial Universe to z~2. This project combines a unique JWST dataset with state-of-the art hydrodynamical simulations and machine learning techniques to understand the origins
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the opportunity to lead manuscript and grant writing and submit publications as the primary author. Responsibilities: Effectively design, implement, and evaluate machine learning and computational
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semantic representation models for sign language. Such representations are key to allowing SL to be efficiently processed by large language models (LLMs), and will lead to machine learning models that can
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to conduct multidisciplinary research around robot learning for autonomous robotic chemists, with a background of excellent research outputs across Robotics and Machine Learning, ideally with a background in
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, artificial intelligence/machine learning, digital twins, and blockchain technology for operations and maintenance. This position is part of the Maritime Future Fuels Training Plan project, which aims
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for safety-critical bilateral teleoperation. The research will leverage a combination of passivity-based control methods and machine learning techniques to enable reliable and robust teleoperation in uncertain