ACAD107869

An 18-month Postdoctoral Research Associate (PDRA) will work on a NERC-funded project quantifying the role of vegetation legacy to climatic extremes (i.e., droughts and heatwaves). This project integrates statistical data synthesis (flux, satellite and manipulation experiments) with process-based modelling to deliver new process-orientated insight into how trees will responds to projected changes in the frequency, magnitude, and duration of future droughts and heatwaves across Europe. The role has the option to EITHER focus on machine-learning approaches or land surface model development. The PDRA will be based in the research group of Martin De Kauwe (https://mdekauwe.github.io/).What will you be doing? You have the option to EITHER focus on:

• Process-based model development of the UK's land surface model, JULES (the Joint UK Land Environment Simulator). These developments will include the representation of plant hydraulics and testing novel hypotheses relating to photosynthetic acclimation to temperature, leaf acclimation to hydraulic impairment, partial xylem embolism legacy, and the role of non-structural carbohydrates in recovery.
• Statistical machine learning to 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.
  
  PhD (or soon to be awarded) in vegetation modelling, plant ecophysiology, mathematics, physics, atmospheric science, or similar quantitative discipline.
• Knowledge of plant physiology processes.
• Significant experience in the use of relevant terrestrial biosphere models (modelling position) or machine learning approaches (statistical position).
• 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.