Hydrodynamic models are the main tools for predicting the behaviour of flow in rivers. Due to the simplifying assumptions and various sources of uncertainty, including uncertainties associated with model inputs, model parameters and model structure, the outcomes of these models are affected by a certain degree of randomness, and hence rarely show a perfect resemblance to the real system behaviour. The usefulness of the model outcomes is affected by the type and magnitude of uncertainties that inherently exist and cannot be eliminated.
Perhaps, the most significant challenge in making hydrodynamic models applicable to design and management problems is determining the values of its many parameters and evaluating the associated uncertainties. Every model includes a number of parameters that are often difficult or costly to measure and also a number of conceptual parameters, which due to lack of an accurate physical meaning, cannot be directly measured.
The aim of this research is to exploit advanced state-of-the-art evolutionary computation and Bayesian statistical techniques to develop a probabilistic calibration framework to be used in the field of and civil and environmental engineering. The primary output of this research will be a better understanding of the uncertainties inherent in hydrodynamic and flood inundation model predictions.
The successful candidate is expected to have a first class degree in Civil Engineering or a related discipline, preferably with a good knowledge of Water engineering. Applicants should also have a strong understanding of hydraulic principles and a good knowledge of statistical analysis. Familiarity with computer programming is essential to this project.
Informal enquires can be sent to Dr Soroosh Sharifi ([email protected]
) and in the first instance should contain a covering letter and a CV.
For excellent applicants (very good first degree), there is the potential for funding for Home / EU students that will cover fees at the current Home / EU student rate and a stipend. Overseas students are welcome to apply but should note that they will be required to be either completely self-funding, or to make up the difference between Home and Overseas fees.