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Field
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for understanding their reliability and for making informed decisions based on their predictions. This project aims to develop new methods for uncertainty quantification in mathematical and statistical models
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the quantification and representation of uncertainty within and between these models. Addressing these questions is essential if their predictions are to be used for scientific understanding and societal decision
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Description The main tasks to be carried out by the selected candidate will be the following: ● Develop new methods for the uncertainty quantification of non-linear statistical models using the 'online
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Are you interested in developing mathematically grounded methods for uncertainty quantification in deep learning, particularly for large language models in healthcare applications? Are you looking
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RAP opportunity at National Institute of Standards and Technology NIST Mathematical Modeling, Analysis, and Uncertainty Quantification Location Information Technology Laboratory, Applied and
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the surrogate forward models with a Bayesian inverse modeling framework to achieve real-time or near-real-time uncertainty quantification, such that we can efficiently resolve the uncertainties rising from rock
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Are you fascinated by application-oriented research in mathematics and eager to work at the interface of numerical optimization, optical design, and uncertainty quantification? In this PhD project
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, you will focus on the quantification of model uncertainty, that arises from limited knowledge and the quantification of data uncertainty, that arises from data-inherent variability. For this, you can
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to international scientific conferences and to industry partners (Airbus). Your work will focus on developing integrated computational tools for structural wing design, linking advanced failure models, uncertainty
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NIST Nanostructure Fabrication and Measurement Group | Gaithersburg, Maryland | United States | about 6 hours ago
, or other microscopy methods; instrument-sample interaction modeling; nanofabrication; inverse problems; statistics and uncertainty quantification; scientific machine learning; semiconductor metrology