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Field
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models, hybrid ML-physics models, and climate emulators are rapidly becoming an integral part of the prediction landscape. While these approaches can achieve remarkable predictive skill, fundamental
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real-time process signals. Using battery-related hydrometallurgical leaching as a model system, the project combines controlled experiments, real-time monitoring, chemical analysis, and data-driven
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defenses in Large Language Models (LLMs). Distributed Machine Learning : Specialization in data parallelism, model-parallelism, and collective communication strategies in large-scale environments
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lithium iron phosphate (LFP) batteries. Key Responsibilities: Develop and implement machine learning algorithms for SOC and SOH estimation. Analyze large datasets from battery systems to improve model
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Percentage of Time 20 Job Duty Digital Intervention Development (Chatbot/Tools). Support design and development of AI-driven chatbot interventions for burnout mitigation Integrate predictive model outputs
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long autoregressive time series, diagnostics and model comparison, sensitivity and identifiability analysis, intuitive model interrogation methods and calibration when observations are few or
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information. We are particularly interested in approaches that move beyond conventional model performance to evaluate reliability, transportability, uncertainty, information sufficiency, and clinical usefulness
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evolutionary simulations) to establish a predictive framework linking the evolution of learning in social decision-making to social evolution. It will include the development of simulation code using a
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the drop along said trajectory The project will yield a predictive framework that can suggest optimum ‘spatio-temporal landscapes’ of chemical or flow or electric fields, to steer an active drop along
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) is an interdisciplinary, collaborative research program housed at the University of Idaho. IMCI creates an intellectual and physical environment that fosters model-based interdisciplinary research