Call for Post-Doctoral Fellow for a project on Data Fusion and Calibration Methods for Spatial Risk Analysis.

Work Plan: The objectives of the work plan are to propose methods for calibrating extreme values of simulated data based on observed data. This problem is extremely important due to its applicability to several areas such as energy and environment. In an environmental framework, extreme values of certain variables, such as wind speeds and precipitation may cause severe damages in property, namely in electrical networks, roads, and agricultural infrastructures. Usually the observed data are scarce and thus simulation is the usual alternative. However, although the simulated and observed data are similar in the bulk of the distribution they are often very different in terms of extreme values. Simulated data have typically lighter right tails than those of observed data. Risk maps, based on simulated data, tend to underestimate the probabilities of damage. Methods for data fusion and calibration provide information which is more reliable from a spatial point of view and will produce more accurate risk maps.

Contract Duration: 31 months.

Place of Work: Work will mainly be developed at the facilities of Research Center CEAUL (Centre for Statistics and Applications), in the Faculty of Sciences, University of Lisbon, Portugal. Some work may be developed over research stays at the Centre for Statistics, University of Edinburgh, UK.

Web: Further details are available from

https://www.maths.ed.ac.uk/~mdecarv/dfus/files/call_postdoc.pdf

For queries contact FillGapsInMaps@gmail.com, Patrícia de Zea Bermudez (pcbermudez@fc.ul.pt) or Miguel de Carvalho (miguel.decarvalho@ed.ac.uk).