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Field
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. Experience with uncertainty quantification and multi-modal deep learning. Experience with distributed training. Skill in written and oral communications. Experience interacting with scientific staff and
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geographic patterns of dominant water level components over seasonal-to-interannual time scales. Develop methods for quantifying uncertainties in total water level estimations, considering coastal morphology
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weather events such as floods, droughts, and heatwaves, integrating probabilistic approaches to account for uncertainties. Use data assimilation techniques to combine observational data with AI models
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of decarbonization policy pathways for electric power systems. Desired experience includes optimization and simulation of electric power systems under uncertainty, programming in Python, Julia, and/or MATLAB, and
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evaluating data visualization techniques for communicating statistical information, especially uncertainty information, to wide audiences. Candidates will draw on expertise in data visualization and HCI, and
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, data science, mathematics, or closely related fields (e.g., physics) Demonstrated experience with data science, especially statistical hypothesis testing, uncertainty quantification, pattern analysis
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, carbon capture and utilization, transportation electrification, energy transportation, energy water systems, environmental/health impacts, etc. Experience in decision-making under uncertainty and/or life
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operation are pivotal in laboratory environments; thus, guarantees for correct behavior despite uncertainties must be provided. This project will integrate visual sensing and force sensing to obtain effective
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, Uncertainty quantification, Approximation Theory, Applied Probability and Bayesian statistics, Optimal Control and Dynamic Programming. Appointment, salary, and benefits. The appointment period is two years
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PhD/Postdoc position in trustworthy data-driven control and networked AI for rehabilitation robotics
control of such systems, taking particularly into account model uncertainties as well as limitations pertaining to acquisition of data, communication, and computation. We apply our methods mainly to human