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areas. These include, but are not limited to: Applying machine learning algorithms to solve real-world problems. Creating and structuring databases for storage, retrieval, and image analysis. Determining
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measurements, airborne measurements from field campaigns (e.g., NASA CAMP2EX, ARCSIX) or surface observations. Research involving machine learning techniques will be strongly encouraged. Field of Science: Earth
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candidates who would like to learn and gain experience in how to plan and perform large animal research and/or burn research in a military medical environment. What will I be doing? As an ORISE participant
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• Unmanned Aerial System development, testing, and research • Biomechanics research in musculoskeletal and gastrointestinal systems • Machine learning • Mechatronics and robotic systems • Wind Tunnel
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project), create a unique opportunity to apply machine learning and neural network methodologies, in conjunction with simplified ice sheet models, to advance understanding of ice sheet basal processes and
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development to support extended reality technologies, machine learning pipeline integration, integration of sensors/devices to mobile platforms, and creating novel clinical decision support applications for our
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. Develop skills in coupling crop and hydrology models at watershed scales. Gain experience validating models using large, multi-source datasets. Learn to apply high-performance computing and machine learning
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generated quickly and regularly. Help develop machine learning techniques for feral swine abundance in data sparse environments. Collaborate with APHIS Wildlife Services (WS) to integrate data and model
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of interest include: • Machine learning for classification of astrophysical signals • Artificial intelligence augmentation of spaceborne observatories to reduce data transmission rates • Migration of science
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to multidisciplinary research aimed at advancing military medicine. What will I be doing? This opportunity offers a hands-on learning experience within a collaborative research environment focused on combat casualty