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Synopsis

Humid heat waves are one of the largest emerging hazards as the planet warms. However, there is still limited understanding of the excess mortality associated with humid heat in data-sparse regions of the globe, including Southeast Asia and Africa, which are likely to be the worst hit. Machine learning alongside detailed urban modelling provides a new opportunity to quantify and predict potential excess mortality across the globe. This work will be part of a larger project that will produce a global early warning system for deadly heat waves in collaboration with Oxford University and ANU.

Aims

Develop and use machine learning models to translate climate projections into temperature and heat conditions that people will experience in large cities.

We’re looking for a student with:

• A strong background in maths or computer science
• Background in coding and analysing large data sets

Student benefits

• Learning to analyse the output of climate models
• Learning to analyse satellite data
• Learning to develop machine learning models and to use them

Supervisors: Katrin Meissner, Sanaa Hobeichi, Negin Nazarian