PhD Studentship, ecology and evolution, Silwood Park, UK

A PhD Studentship in Quantitative Methods in Ecology and Evolution (QMEE CDT) is being offered through Imperial College London, UK. The project title is "Plant-atmosphere interaction in near-real time: eco-evolutionary optimality theory applied to carbon-cycle forecasting".


Silwood Park is the rural campus of Imperial College London and is situated near the village of Sunninghill, near Ascot in Berkshire.

The four-year, fully-funded PhD is offered as part of the Quantitative Methods in Ecology and Evolution Centre for Doctoral Training (QMEE CDT) and will be jointly supervised by Prof. Colin Prentice (Imperial College, Silwood Park) and Prof. Sandy Harrison (University of Reading) with support from Dr. Anna Agustí-Panareda and Dr. Gianpaolo Balsamo (ECMWF).


The terrestrial biosphere regulates the energy, water and carbon exchanges between the land and the atmosphere. Vegetation is extremely sensitive to changes in weather conditions on daily, seasonal and annual timescales; in turn, changes in vegetation properties control CO2 and latent and sensible heat fluxes, with rapid feedbacks to the state of the atmosphere.

The European Centre for Medium-Range Weather Forecasts (ECMWF) performs a near-real-time quantification of atmosphere-land CO2 fluxes as a component of its operational Integrated Forecast System (IFS) for weather, and for atmospheric composition as part of the Copernicus Atmosphere Monitoring Service (CAMS).


The project’s aim is to make this quantification both simpler and more accurate, by deploying a new ecosystem model that combines satellite observations with eco-evolutionary optimality theory to predict canopy-level conductance and photosynthesis. The principle of the model is that over a time scale of days, photosynthetic characteristics of leaves ‘acclimate’ so as to simultaneously minimize costs (for carbon fixation and water transport) and maximize net carbon uptake.

The project will undertake a comprehensive assessment of the model’s predictive skill for plant traits related to photosynthetic function, and will incorporate the model into the ECMWF land-surface modelling framework. Through its inclusion in the IFS, the model will lead to improved near-real-time forecasts of the physiological state of vegetation and the exchanges of energy, water vapour and CO2 between the atmosphere and land.


The ideal candidate for this project would have a Masters in an appropriate science, good programming and numerical skills, an understanding of plant ecology and an interest in applying mathematical theory to solve real-world problems. Good team and communication skills are essential.


Applications should be submitted to This email address is being protected from spambots. You need JavaScript enabled to view it. by 29 June 2018.

Further information

For more information about the project or the QMEE CDT, contact This email address is being protected from spambots. You need JavaScript enabled to view it. or This email address is being protected from spambots. You need JavaScript enabled to view it..