PhD Studentship: Integrating sensing, modelling and data analytics to understand forest microclimate dynamics under fire, degradation and climate change in Amazonia
University of Exeter
This project combines environmental sensing, computational modelling and data analytics to understand how climate change, wildfire and land use are reshaping tropical forest microclimates. Tropical forests are warming and drying from the inside. Wildfire, degradation, land-use change and climate extremes open the canopy and alter the microclimate beneath it, increasing tree mortality and making forests more flammable. Predicting these changes requires measurements and models that work at the fine scales where tr...
This project combines environmental sensing, computational modelling and data analytics to understand how climate change, wildfire and land use are reshaping tropical forest microclimates. Tropical forests are warming and drying from the inside. Wildfire, degradation, land-use change and climate extremes open the canopy and alter the microclimate beneath it, increasing tree mortality and making forests more flammable. Predicting these changes requires measurements and models that work at the fine scales where tr...
Opportunity details
This project combines environmental sensing, computational modelling and data analytics to understand how climate change, wildfire and land use are reshaping tropical forest microclimates. Tropical forests are warming and drying from the inside. Wildfire, degradation, land-use change and climate extremes open the canopy and alter the microclimate beneath it, increasing tree mortality and making forests more flammable. Predicting these changes requires measurements and models that work at the fine scales where trees live and die, yet both remain poorly tested in the tropics. Low-cost sensors drift, fail and vary with design and placement; physics-based microclimate models are rarely validated against dense field data; and scaling point measurements up to landscapes introduces uncertainty that is seldom quantified. This PhD will develop and test an integrated sensing and modelling framework for predicting fine-scale climate in complex, data-sparse environments, using Amazonian forests as a demanding test case. It has four linked objectives: Sensor performance and uncertainty: quantify how sensor design, placement, drift and data gaps affect microclimate measurements, by testing established dataloggers against custom-built and industry-partner prototype devices. Model validation and improvement: test physics-based models of energy, heat and water exchange between atmosphere, canopy and soil against measured vertical profiles, from soil to canopy, across intact, burned and degraded forest. Data fusion and machine learning: combine in-situ sensor data with airborne and spaceborne Lidar (e.g. GEDI) and optical satellite data, using machine learning to calibrate low cost sensors and correct systematic model errors. Upscaling with quantified uncertainty: propagate uncertaint...
Eligibility
The successful candidate will join an international UK–Brazil research team at Exeter, with links to the University's strengths in weather and climate science and data science.
Requirements
The successful candidate will join an international UK–Brazil research team at Exeter, with links to the University's strengths in weather and climate science and data science.
Funding and benefits
Funding eligibility: UK Students, International Students. Funding amount: £21,805 The studentship will provide funding of fees and a stipend of £21,805 per annum for 2026/7..
How to apply
Upscaling with quantified uncertainty: propagate uncertainty from the sensor to landscape-scale predictions, and apply the framework to forecast where warmer, drier conditions put trees under heat and drought stress and raise fire risk, including during ENSO related droughts.
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Recorded source check: October 28, 2026. A stored check date does not guarantee that every field is complete or still current. Compare the provider, eligibility, funding and deadline with the live official call before applying.
Benefits
Funding eligibility: UK Students, International Students. Funding amount: £21,805 The studentship will provide funding of fees and a stipend of £21,805 per annum for 2026/7..
Eligibility summary
The successful candidate will join an international UK–Brazil research team at Exeter, with links to the University's strengths in weather and climate science and data science.
Requirements
The successful candidate will join an international UK–Brazil research team at Exeter, with links to the University's strengths in weather and climate science and data science.
Documents required
Language/test requirements
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