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PhD Studentship - Transform Global Carbon Science: Model Tropical Vegetation to Reshape Carbon Budgets (PhD funded UK fees)

University of Exeter - ESE

Qualification Type: PhD
Location: Exeter
Funding for: UK Students
Funding amount: UK home tuition fees and an annual tax-free stipend of at least £21,805 per year
Hours: Full Time
Placed On: 13th February 2026
Closes: 19th April 2026
Reference: 5825

The Problem Worth Solving Tropical forests absorb a quarter of human carbon emissions. Yet we're losing them faster than we can understand them. Deforestation, degradation through fire, and climate extremes are destroying these vital ecosystems while simultaneously exposing a massive blindspot in climate science: our models fail to accurately predict how tropical vegetation responds to disturbance.

This uncertainty cascades into the Global Carbon Budget—the gold standard dataset guiding international climate policy. The largest uncertainty (±60%) stems from land-use and land-cover change in tropical regions, where we've lacked the tools and data to quantify emissions from deforestation and degradation with sufficient accuracy. The result? Climate predictions with unwanted uncertainty at the precise moment we need precision most.

Your Challenge Join CLARiTy, an international research consortium combining cutting-edge datasets, process-based modelling, and remote-sensing data to reduce land flux uncertainties by an order of magnitude. In this fully funded PhD (UK home fees), you'll develop next-generation Dynamic Global Vegetation Models (DGVMs) that accurately represent tropical forest and savanna carbon cycling and vegetation recovery from disturbance.

The Science You'll develop and run JULES—a sophisticated process-based land surface model coupling vegetation dynamics with carbon cycling. The work is mathematically rigorous: you'll integrate novel datasets on vegetation function, build mechanistic representations of ecosystem responses to disturbance, and constrain models using empirical and remote-sensing observations. You will be developing process-understanding that translates complexity into predictive power.

Your results will directly improve the Global Carbon Budget, influencing climate policy at UNFCCC COP meetings annually. Real impact from rigorous science.

What You'll Do

  • Develop process-based representations of tropical vegetation dynamics and carbon cycling
  • Integrate empirical datasets and state of the art remote-sensing observations into JULES DGVM
  • Write sophisticated code (Python, R, Fortran) to simulate complex ecological and biogeochemical processes
  • Analyse model outputs to understand vegetation recovery from fire, drought, and deforestation
  • Contribute to reducing 60% uncertainty in land-use change emissions

Who You Are

  • Mathematically and scientifically rigorous thinker
  • Strong coding skills (Python, R, Fortran, or equivalent)
  • Excellent analytical abilities and problem-solving approach
  • Background in Mathematics, Earth System Sciences, Natural Sciences, or related disciplines
  • Driven by intellectual challenge and real-world application
  • Collaborative but independent learner

Why This Matters Climate models shape policy. Your models will inform international climate agreements, conservation strategies, and humanity's response to climate change. You're working at the intersection of mathematics, ecology, and climate science—where clever thinking translates directly into global impact.

The Package Fully funded PhD (UK home fees and stipend during 3.5 years). Access to world-class datasets and complementary NERC-funded projects. Mentorship from leading climate scientists. International collaboration through CLARiTy consortium. Specialist workshop on tropical vegetation dynamics in South Africa (April 2026). Training in cutting-edge modelling, remote-sensing, and climate science.

Ready? If you're intellectually ambitious, mathematically rigorous, and want to solve one of climate science's most pressing problems, apply now. This is high-impact science for clever minds.

Please apply via the ‘Apply’ button above.

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