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Field
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you like best. Uncertainty plays a key role in both learning1–4 and decision making5, especially when the environment is not stationary (e.g., a new brand now runs the nearby coffee shop and you like it
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machine learning and Bayesian calibration methods to enable multi-scale, multi-physics model development. Complete simulation verification, model validation, uncertainty quantification, and documentation
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carbonate and lithium hydroxide production, hydrometallurgical processes, and battery recycling, while applying sensitivity, uncertainty, and scenario analyses to identify key process and supply-chain drivers
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Biophysical and Biomedical Measurement Group, National Institute of Standards and Technology | Gaithersburg, Maryland | United States | 2 months ago
uncertainty. What you will do: - Design, assemble, and characterize DNA origami nanostructures that undergo controlled conformational changes upon target binding and/or applied electrostatic bias. - Develop and
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uncertainties in warming feedbacks on methane, provide improvements that can advance a process-based understanding of feedbacks, and make connections with existing or potential mitigation interventions to reduce
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designs and regional-to-continental scale investment of monitoring resources. Developing methods to quantify and propagate uncertainty across integrated monitoring frameworks Developing methods for joint
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of uncertainty and complexity. Applicants must have: A PhD degree in a relevant field, e.g. urban geography, urban planning, innovation studies, (social) design (research), public administration
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funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Contrails are aviation sector’s invisible climate threats with large uncertainties
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to the development of scalable, explainable, and uncertainty-aware AI methods that enhance model robustness, reliability, and scientific discovery. Publish research findings in high-impact journals and present results
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validating uncertainty-aware AI models for real-time crash risk prediction with guaranteed confidence bounds, interpretability, and fairness-by-design, using a blend of centralized and federated learning