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.
|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|