Research Associate in Statistical Machine Learning

The role

The Postdoctoral Research Associate will work on a new NERC-funded project: ‘Ghosts from summers past: quantifying the role of vegetation legacy to climatic extreme’. This project will combine statistical data synthesis from flux data and manipulation experiments with modelling to deliver new process-orientated insight into the response of trees to projected changes in the frequency, magnitude, and duration of future droughts and heatwaves across Europe. One role will involve detailed process model development and the other, the application of machine learning to characterise the timescales of ecosystem recovery.

The Postdoctoral Research Associate will join the research lab of Associate Professor Martin De Kauwe at the University of Bristol. The Postdoctoral Research Associate will also work closely with other project investigators: Professor Tristan Quaife (University of Reading), Professor Patrick Meir (University of Edinburgh), and Associate Professor Lucy Rowland (University of Exeter), as well as a team of international grant partners. 

What will you be doing?

The statistical machine learning role will identify multi-scale (experiment, field, landscape), spatial and temporal legacy timescales of drought and heat extremes. These data-driven insights of legacy timescales will be leveraged alongside model hypothesis testing (above) to refine new mechanistic theory on drought legacy/recovery.

You should apply if

• PhD (or soon to be awarded) in mathematics, vegetation modelling, physics, atmospheric science, or similar quantitative discipline.
• Significant experience in the use of machine learning approaches
• Demonstrated programming/computing skills in a Unix/Linux environment.
• Strong research and publication track record (relative to career opportunity)
• Excellent verbal and written communication skills.
• Demonstrated ability to work effectively as part of a team and independently in a research environment.

Informal expressions of interest can be directed to Martin De Kauwe, m.dekauwe@bristol.ac.uk.

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