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) in the department and in the Great Plains IDeA-CTR network, and growing institutional strengths in AI, machine learning and clinical informatics. This is a unique opportunity to translate and expand
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thermomechanical process simulations such as casting and welding. The research activities at SDU-ME spans widely from fluid mechanics, condition monitoring, machine learning, fatigue, maritime structures
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of working in a multidisciplinary team. Previous experience with deep learning and computer vision techniques applied to robotics, biological or ecological research, or environmental monitoring; including
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Institute (https://cse.umn.edu/aiclimate). The role involves building knowledge-guided machine learning (KGML) models for sustainable agricultural practices, developing AI-ready benchmark datasets, and
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basis of age (40 and over), color, disability, gender identity, genetic information, marital status, domestic partner status, military or veteran status, national origin/ancestry, race, religion, creed
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for data-efficient exploration and optimization within the process parameter space as well as for adaptive, data-driven machine learning to map the electrolysis process to a digital twin. Data workflows and
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status or military obligations, sexual orientation, gender identity or expression, genetic information, national origin, race (including hair texture and protected hairstyles such as natural hairstyles
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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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N flux dynamics. NitroScope will make a combined use of different measurement techniques to reduce uncertainties and biases in N flux monitoring, including proximal sensing, remote sensing, Eddy