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Field
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methods and software, including uncertainty methods, spatialized approaches, and integration with dynamic modeling frameworks. Experience applying or developing mechanistic animal models, and/or whole farm
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inference pipelines for soft biomechanical systems, including differentiable physics engines, to support interpretable analysis, parameter estimation, sensitivity studies and uncertainty quantification. By
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and global sourcing strategies, taking into consideration of various risks and uncertainties. Partner with an interdisciplinary team to support creation of supply chain databases and develop user
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Biogenic volatile organic compounds (VOCs) have a profound impact on local and regional climates. Yet, there is high uncertainty of this impact due to the lack of reliable estimates for natural
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commercialization decisions, and overcome marketplace challenges. The programming uniquely addresses the high uncertainties, costs, challenges and long time-frames typically associated with science translation. It is
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. • Familiarity with vibration data analysis techniques. • Experience with Monte Carlo simulation, uncertainty quantification, or sensitivity analysis. • Programming skills in Python, MATLAB, R, or similar
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, data analysis and programming, preferably in Python you have knowledge of climate adaptation, infrastructure resilience, decision making under (deep) uncertainty or cost benefit analysis you have
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) methods for modeling and optimization of metallic materials and advanced manufacturing processes. Participate in the design of integrated, scalable numerical methods and uncertainty quantification. Follow
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with one or more of: Health claims data, EHRs, or other large-scale health/administrative datasets Environmental, climate, or air pollution exposure data Causal inference methods Uncertainty
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Perform continuous and event-based hydrological simulations using weather generator output and extreme rainfall scenarios Compare the performance, robustness and uncertainty of different methods Communicate