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physics-based machine learning for land cover forecasting. Intended use of these capabilities include urban planning, hydrological modeling, and wildfire risk mitigation strategies. In addition to model
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research associate who will focus on machine learning, signal processing, and statistical analysis with emphasis on prognostics and applications. This position resides in the Accelerator Science and
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strategies. Within the GeoAI group, you will have the opportunity to develop and publish on novel machine learning based solutions to national and global problems. Research activities include the design of
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analysis, and information flow. Major Duties/Responsibilities: Development and support of data collection, modeling, and integration, along with associated computational and machine/deep learning
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) is seeking qualified applicants for a postdoctoral position in machine learning and surrogate models. Areas of interest include graph neural networks, federated learning, data-driven model reduction
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operations by raising safety concerns, using a questioning attitude, considering hazards for every task, and never stopping learning. Participate in assessments, critiques, incident investigations, and related
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together, and measure success. Basic Qualifications: A PhD in energy science, computer engineering, or a related field and at least three years of relevant experience. An undergraduate degree in physics
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(AI) and machine learning (ML). We are specifically seeking a candidate with solid experience with handling multimodal healthcare data. We are looking for someone with innovative thinking to design and
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Postdoctoral Research Associate - Structural Simulation and Machine Learning (ML) for Polymer Compos
manufacturing technologies through machine learning and physics-based simulations, specifically finite element analysis (FEA) for polymer composites. The candidate will also focus on developing a manufacturing
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technologies Field, laboratory, or numerical testing of hydraulic structures Geospatial and remote sensing data analysis (e.g., GIS, Google Earth Engine) Machine Learning applications in hydrology/water