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Field
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Postdoctoral Research Associate will be responsible for AI-driven materials discovery. The position will collaborate with interdisciplinary teams to develop integrated molecular simulation and machine learning
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). The work spans: Close collaboration with researchers in neuroscience, engineering, and ethics. Human-in-the-loop brain/muscle-machine experimentation focused on learning pedagogy for adaptive neural systems
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in Machine Learning, Computer Science, Electrical Engineering, Geophysics, Applied Mathematics, or a closely related field. Demonstrated strong research skills, evidenced by high-quality publications
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machine learning within the Department of Electrical and Computer Engineering. The Postdoctoral Fellow conducts independent and collaborative research, publishes findings in peer reviewed conferences and
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Remote Sensing; Machine Learning Models for Predicting Wildfire Spread; Wildfire Risk Assessment Through Multi-Modal Data Integration; Automated Vegetation and Fuel Load Mapping Using Computer Vision; AI
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join the group to develop AI and machine learning based software to assist clinical workflow and pre-clinical studies. Required Qualifications: Ph.D. in a physical science or engineering field Strong
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for this position will work as a member of an interdisciplinary team led by Dr. Colin Xu on Department of Defense (DoD)-funded research project involving the use of statistical and machine learning methods
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. Responsibilities may include: Designing and conducting studies on the clinical impact of GLP-1 and other metabolic therapies Developing and applying computer vision and machine learning techniques to analyze
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learning. Job responsibilities will include: Develop simulation algorithms and software to model challenging gas adsorption behavior in porous materials Develop novel machine learning model for predicting
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 3 hours ago
inventories) with satellite remote sensing data (e.g., spaceborne lidar and/or hyperspectral observations) and apply machine learning and deep learning approaches to address these questions. This position is