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Field
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-based modeling of hydrological and Earth system processes. The CHAS group conducts world-class research in hydrological and Earth system modeling, large-scale data analytics and machine learning (ML), and
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involve the integration of: Advanced motion planning and control algorithms Multi-modal perception techniques (e.g., vision, tactile, force) Machine learning models for physical behavior prediction and
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develop statistical and machine learning models to identify and validate predictive biomarkers of resistance evolution in Pseudomonas aeruginosa lung infection. As part of this work, the postholder will
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for materials discovery. Project description Machine learning opens up new opportunities to accelerate the discovery of next-generation energy materials by combining predictive and generative approaches. In
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Position Summary This position will focus on integrating high-resolution field monitoring, remote sensing, and statistical and numerical modeling approaches to improve predictive flood hazard
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skills (one or more of the following strongly desired) Exploratory analysis of massive datasets (machine learning methods) Spatial data analysis and Geographic Information Systems (GIS) Forecasting and
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, mathematics, computer science, engineering or a related discipline Required Other None Additional Preferred Experience working in one of the following areas: Machine learning/predictive modeling
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to the specification and analysis of data from major space missions (SMOS, Biomass, Venµs, Trishna) and develops models capable of describing and predicting the evolution of continental surfaces under various pressures
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primarily research on Reinforcement Learning, and/or Optimal Control, and/or Model Predictive Control. RISC invites qualified applicants in the areas of electrical, computer, or mechanical engineering, or
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machine learning methods. Provide theoretical predictions to guide experiments, and atomic-scale physical understanding to experimental observations. Publishing findings in peer-reviewed journals