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. These models will improve predictions of mineral alteration, scaling and long-term reservoir performance while reducing uncertainty in geothermal development. Specifically, you will: Integrate core, wireline log
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statistical and machine learning, deep learning, chemometrics, multimodal data fusion, computer vision, uncertainty-aware modeling, stochastic control, optimization, and deployable edge-to-cloud decision
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uncertainty estimates to support reliable deployment in real-world building management systems For further information on the project you can contact your future superior: https://www.fz-juelich.de/profile
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Hydroinformatics – Uncertainty-Aware Optimisation for Robust Water Distribution System Operation The PhD candidate will develop uncertainty-aware, model-based multi-objective optimisation methods to support robust
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programme Reference Number AE2026-0291 Is the Job related to staff position within a Research Infrastructure? No Offer Description Portuguese version: https://repositorio.inesctec.pt/editais/pt/AE2026-0291
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on open-set recognition, foundation models, multimodal approaches, and uncertainty-aware, incremental learning to build adaptable AI systems that recognise known species while handling unseen classes
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-uncertainty-bas… Requirements Additional Information Website for additional job details https://www.academictransfer.com/363597/ Work Location(s) Number of offers available1Company/InstituteTU
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through an integrated digital twin workflow for tunnel maintenance decision support. Heterogeneous inspection and monitoring evidence is transformed into uncertainty-aware condition indicators, which
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 1 month ago
and its spatial variability; use of precipitation coherence as a validation and uncertainty-quantification pathway for high-resolution soil moisture from NISAR and future L-band missions (e.g., ROSE-L
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for sustainable transport planning. The purpose of this PhD project is to develop modelling approaches that improve the ability to analyse future desirable transport scenarios under deep uncertainty. A central