2 day Module (Module 1) PhD Early Bird until 30 April

Developing analytical skills in Bayesian inference and climate attribution for environmental health applications.  

Course Description

Climate change is one of the greatest challenges of our time and its effects on human health are already being felt. As we enter an era where climate change is increasingly recognised as a major threat to public health, there is a growing demand for researchers who can assess the health impacts of climate-related hazards. This summer school is essential for researchers keen to progress in this field. Participants will learn how we can scientifically link climate change to health outcomes using advanced methods from environmental epidemiology and climate attribution science.

The week will consist of two modules:

Module 1: (2 days) Probabilistic climate attribution

is a two-day workshop designed for researchers in Climate Change and Environmental Health who are interested in learning about attribution science but have limited or no prior experience.

Module 2: (3 days) Bayesian models for climate and environmental health

Is a three-day workshop aimed at researchers in Environmental and Climate Health who are keen to explore Bayesian modelling for environmental and climate epidemiology.

This summer school is designed for postgraduate students and researchers at any stage in their career who are interested in climate and health attribution and are keen to learn advanced statistical methods.