###An end-to-end computational workflow for uncertainty quantification (UQ) of climate change on coastal flooding
Project ID: 2228bd1177 (You will need this ID for your application)
Research Theme: Information and Communication Technologies
UCL Lead department: Computer Science
Lead Supervisor: Serge Guillas
Project Summary:
Why this research is important: Geo-hazards (e.g., hurricanes and tsunamis) are low frequency high impact events that cause catastrophic damage to life and the economy. The effect of these disasters has been exacerbated by global climate change induced sea-level rise and changes in precipitation. Given the complex nature of the physical phenomena involved and the dense datasets that characterize modelling of their effect on land, a workflow to characterize uncertainties due to varying climate change scenarios is difficult.
Who you will be working with: Apart from the supervisors, you will be working with data scientists from UCL’s Advanced Research Computing Centre, and domain specialists in the specified geo-hazard.
What you will be doing: Given the broad nature of the research area, you have opportunities to steer the project. Your main task is to build a robust computational workflow that links the source of the disaster to its effect on land (e.g., flooding, damage to structures etc.). The scientific component involves devising parameter frameworks for characterizing the geo-hazard and their effects, e.g., functional expansions, dimension reductions etc. The computational component involves studying existing numerical models and codes, frameworks for UQ (Gaussian process), GIS software (e.g. QGIS), integration into a cat model (e.g. OASIS) and coding changes to link them up in a computational workflow. You are expected to disseminate your research via conferences and peer-reviewed publications. Any gaps in your skills/knowledge of tools will be addressed by relevant training.
Who we are looking for: You will have: Essential: Strong logical and analytical skills. Experience in a scientific programming language e.g., Matlab, Python. Desirable: Experience in parallel computing, uncertainty quantification, geographic information systems.