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Field
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synthesis of timed and probabilistic behavioral models for model checking, performance evaluation, and optimization. The overall objective is to establish formal foundations that bridge static engineering
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on the development and evaluation of snowpack and hydrologic models used for avalanche hazard assessment. Applied research that informs avalanche forecasting centers, land managers, and community stakeholders. You
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Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more efficient, intelligent, and impactful. You will integrate field observations—including
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to artificial-intelligence-driven monitoring systems to forecast and manage stored product insect populations and insecticide resistance. Home to Kansas State University and The Flint Hills, Manhattan, KS
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qualifications: Experience with data assimilation, probabilistic machine learning, Bayesian inference, inverse modeling, and/or simulation-based inference is an advantage. Experience with land-surface models
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directly support nowcasting (for enhanced situational awareness), forecasting (to assess plausible outcomes in a business-as-usual scenario), the evaluation of control measures, and formal cost-effectiveness
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; contribute to member-facing forecasting reports, white papers, and policy briefs. Publish in BEG Reports of Investigation; peer reviewed, international, journals; and informal outlets. Develop and maintain
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, internationally connected research programme spanning Bayesian infectious disease modelling, AI-driven epidemic forecasting, genomic epidemiology, and pandemic preparedness. The postholder will work with Asst. Prof
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is required Desired qualifications: Experience with data assimilation, probabilistic machine learning, Bayesian inference, inverse modeling, and/or simulation-based inference is an advantage
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within the digital twin environment Developing deep learning architectures for time-series forecasting, anomaly detection, and predictive maintenance of the physical asset Designing and training Physics