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pathway planning. You will share your results in stakeholder meetings, scientific conferences, and academic journals. Your work will help Dutch water managers plan adaptation under uncertainty and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
of the position is to help compute the matrix elements that govern the neutrinoless double-beta decay in an accurate way, with controlled uncertainty. Minimum Education and Experience Requirements PhD in physics
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Uppsala University, Department of Information Technology Are you interested in probability theory, statistics, and mathematical modelling? Would you like to develop new methods for uncertainty
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the development of tools enabling local stakeholders to discuss adaptation scenarios, their uncertainties and their social and environmental implications. Working environment — Description of activities
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inconsistencies in video as well as audio. Where to apply Website https://www.academictransfer.com/en/jobs/364624/phd-position-on-explainable-dee… Requirements Specific Requirements An MSc degree in electrical
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of digital twins, high-performance computing and AI/machine learning for fusion design and operation; verification, validation and uncertainty quantification for fusion safety and licensing; development
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accident conditions, including model verification and validation, sensitivity and uncertainty analysis, and comparison with experimental results within the framework of international benchmarks (such as NEA
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models for bridging knowledge gaps, data scarcity and uncertainty gaps, and governance gaps related to monitoring the environment on which humans depend. A part of this is **spatial statistics of sensor
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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