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design and evaluation of uncertainty quantification methods, as well as the integration of geostatistical techniques with Machine Learning models, analysing their reliability in fisheries and environmental
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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
of uncertainty quantification techniques for the learnt models. You will also have opportunities to contribute to open-source computational tools and datasets, teach master-level courses, and advise doctoral
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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
systems as well as towards designing observer-based state estimators from output timeseries data measurements. The research also involves development of uncertainty quantification techniques for the learnt
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, or quantum-inspired methods Experience with hybrid quantum–classical algorithms or optimization methods Background in uncertainty quantification, reduced-order modeling, or machine learning Experience
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collaboration with local water utilities and software developers Integrate digital urban water twins with data, applying methodologies for data assimilation, parameter estimation, and quantification of model
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Associate in a university to work on a specific project. Working alongside the Digital Team at MIRICO, the Algorithm developer will develop a new algorithm to enhance the localisation and quantification
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science, uncertainty quantification, and optimization/operations research to evaluate and recommend carbon reduction strategies under cost, schedule, and quality constraints. o Create decision-support
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lack reliable uncertainty quantification. The methods developed in the project will tackle these shortcomings, enabling computationally efficient inference and prediction of gas dynamics at high spatial
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candidates with strong expertise in Bayesian methods, uncertainty quantification, and/or machine learning applied to nuclear theory. The group’s research spans a wide range of topics including nuclear
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and health. The lab conducts method-driven research in natural language processing (NLP) and artificial intelligence (AI), with an emphasis on LLM reasoning, uncertainty quantification, interpretability