PhD Studentship: Integrating sensing, modelling and data analytics to understand forest microclimate dynamics under fire, degradation and climate change in Amazonia

University of Exeter; Exeter; On-site; GBP 20,000 - 24,000; Full time

Benefits offered by this job

Fees funded

Stipend £21,805 per annum

Job summary

The University of Exeter invites applications for a PhD studentship to develop an integrated sensing and modelling framework for predicting fine-scale climate in tropical forests. The project links environmental sensors, physics-based models and machine learning, with data fusion from Lidar and satellite sources.

Successful candidates will join an international UK–Brazil team, with opportunities for fieldwork in Brazil or desk-based study using existing datasets.

Qualifications

• Training in sensor design and calibration.
• Physics-based and statistical modelling.
• Machine learning and geospatial analysis.
• High-performance computing in R and Python.
• Reproducible research practices.

Responsibilities

• Lead development of an integrated sensing and modelling framework for predicting fine-scale climate in complex environments.
• Test sensor performance and data quality in field and lab settings.
• Fuse in-situ data with airborne/spaceborne data (e.g., Lidar and satellites) for calibration.
• Quantify and propagate uncertainty to landscape-scale predictions.

Skills

Python

R

Geospatial analysis

Machine learning

Data analysis

High-performance computing

Tools

Python

Job description

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 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.

The project builds on the large environmental sensor network and datasets of the UKRI-funded Amazon PyroCarbon Project, which records soil temperature, soil moisture and near-surface air temperature across forests with contrasting fire and land-use histories. These are complemented by open remote sensing and land-use data, and by the open-source microclimate models developed by Prof Maclean's group, already validated across tropical forest sites worldwide.

An industry sensor partner will support testing of prototype devices, offering a possible route to a placement. The doctoral researcher will take a leading role in shaping the project, and can emphasise sensor development, modelling or data science according to their interests. Training will cover sensor design and calibration, physics-based and statistical modelling, machine learning, geospatial analysis and high-performance computing in R and Python, and reproducible research practice. Fieldwork in Brazil is possible (subject to research permits); alternatively, the project can be completed as a desk-based study using existing data and ongoing data collection by partners across Amazonia.

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. The methods developed will transfer well beyond the tropics, to environmental monitoring in agriculture, cities, conservation and wildfire management.

Funded by: EPSRC Engineering and Physical Sciences Research Council

£21,805. The studentship will provide funding of fees and a stipend of £21,805 per annum for 2026/7.