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CSIRO Postdoctoral Fellow - Computational Modelling of Natural Hazards

EmployerCSIRO

LocationMelbourne, Australia

Application DeadlineMay 28, 2017

Job Description

An exciting opportunity to conduct novel research with real-world impact

Use your modelling expertise to help predict natural hazards and potentially save lives

Join CSIRO and kick-start your research career

The Position

CSIRO offers PhD graduates an opportunity to launch their scientific careers through our Postdoctoral Fellowships. These fellowships provide experience that will enhance career prospects and facilitate the development of potential leaders for CSIRO.

Data61 in collaboration with CSIRO Oceans & Atmosphere, is seeking to appoint a highly motivated Postdoctoral Fellow with expertise in meteorology and down-scaling of climatic models with associated skills in uncertainty modelling and analysis, to work at the interface between three key scientific disciplines namely 1) physics based modelling of natural hazards, 2) meteorology and 3) statistics, to develop algorithms and methodologies that are able to provide well developed processes to address the key scientific challenges in being able to predict weather systems and translate them into meaningful spatio-temporal representations to reduce/eliminate fatalities, and reduce the economic and social damage caused by natural hazards such as wildfires and flash floods.

Specifically you will be expected to lead the following investigations:

Application of the Conformal Cubic Atmospheric Model (CCAM) for a series of numerical experiments that are focussed on an analysis of the resolution requirements for predictability of localised natural hazard events.

Development of sophisticated statistical models that can provide appropriate uncertainty metrics able to reconcile with the dual uncertainties associated with weather models and spatio-temporal physical models for flood and fire spread predictions.

Evaluation of RADAR based weather data as a mechanism for validating the outputs from CCAM as well as to understand the effectiveness of such outputs for the spatio-temporal prediction of hazard metrics for flash flood and fire events.

To be successful you will need:

A doctorate (or will shortly satisfy the requirements of a PhD) in a relevant discipline area, such as Applied Mathematics, Physics or Engineering with a strong computational modelling focus.

Demonstrated experience in modelling complex environmental/atmospheric systems. This will include managing data, such as netcdf from CCAM.

Demonstrated strong experience in programming using Fortran, C and/or C++ .

A track record of excellence in research output demonstrated through scientific publications, research reports and scientific code development.

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