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 |
| 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 |
