PhD: Tropical Forest Microclimate Sensing & Modelling

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

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.