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Institute for Data Science and Artificial Intelligence

Emulation / Uncertainty quantification

Uncertainty quantification (UQ) is the science of quantitative characterization and reduction of uncertainties in both computational and real world applications (Wikipedia).

If you're looking for a data scientist to work with on a research project or someone to discuss potential methodologies with for a research problem, then you search for the topic you need or alternatively use the A-Z button to search the full list of data scientists.

Name  Expertise 
Peter Challenor  Statistical modelling, Emulation / uncertainty quantification
Saptarshi Das Optimisation, Physical Modelling, Machine Learning, Bayesian Inference, Time Series Analysis, Statistical Modelling, Emulation/Uncertainty Quantification, Frequentist Inference, High Performance Computing, Signal Processing, Control Systems, Image Processing

Richard Everson

Optimisation, Machine learning, Machine vision, Emulation / uncertainty quantification, Signal processing
Jonathan Fieldsend Optimisation, Software engineering, Machine learning, Emulation / uncertainty quantification
Cyril Morcrette Physical Modelling, Machine Learning, Emulation/Uncertainty Quantification, High Performance Computing, Atmospheric Sciences, Environmental Sciences, Meteorology, Atmospheric Physics
Stefan Siegert Spatial statistics, Physical modelling, Software engineering, Machine learning, Bayesian inference, Time series analysis, Statistical modelling, Emulation / uncertainty quantification, Frequentist inference
Krasimira Tsaneva Physical Modelling, Network Analysis, Statistical Modelling, Emulation/Uncertainty Quantification,Time Series Analysis. Experience with applications to Biology, Medicine and Healthcare
Danny Williamson Decision theory, Optimisation, Machine learning, Bayesian inference, Statistical modelling, Emulation / uncertainty quantification