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working with multiphysics and multiscale models and in accelerated methods for solving partial differential equations and scientific machine learning (physics-informed machine learning) is essential. A
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on monitoring and modeling research. • Compile and analyze current scientific data on GHG emissions in freshwater ecosystems, focusing on CO2 and CH4 emissions. • Acquire, organize, and evaluate GHG emissions
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the health professions. Skills: Computer skills with Microsoft® Office Suite and have the capacity to learn other software as needed. Communication skills, both written and verbal. Required Application
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caring learning environment that will promote active learning and foster critical thinking. Essential Duties and Responsibilities Provide Nursing Assistant Instruction. The Nursing Assistant Program
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documentation. - Supporting the team in preparing and implementing various informed machine learning models for benchmarking and analysis. Desirable: - Currently enrolled in a Bachelor's or Master's program in
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, MEG, NIRS, computational modeling, deep neural networks) with a focus on infancy and childhood (0–6 years). Desired content areas are broad and may include learning and memory, language, perception
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of and experience with hydrological and/or hydrogeological modeling - Knowledge of and experience with AI concepts (machine learning, deep learning, and PINNS) and/or digital twin development - Experience
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Education: Ph.D. or M.S. in Computer Science, AI, Computer Vision, or related field Experience: 3+ years in computer vision and deep learning, with specific focus on microscopic imaging, generation
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system design. We employ advanced computational methods, machine learning, modeling, and custom hardware and software to continually test our solutions in various real-world industry projects. In one
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publications in CAV/traffic modelling, computer vision, or reinforcement learning. Hands-on with YOLOv8 + DeepSORT and RL platforms (e.g., FLOW) for traffic control. Excellent English communication - essential