Competition-funded PhD: Regenerative AI for Peatlands

Institute of Mathematics and its Applications; Dundee; On-site; GBP 80,000 - 100,000; Full time

Job summary

A research institute is seeking candidates for a project focused on developing AI models to optimize peatland regeneration in Scotland's Flow Country. Successful candidates will co-design tools with stakeholders, build emulators, and create decision-making frameworks. Ideal applicants will have a strong quantitative background in mathematics, statistics, or related fields, with a keen interest in machine learning and environmental applications. Applications are open until 9 January 2026. Contact Dr. Eric Hall for inquiries.

Qualifications

• Candidates should have a strong quantitative background.
• Experience in machine learning for PDE/simulation models is desirable.
• Interest in climate or environmental applications is preferred.

Responsibilities

• Co-design a Regenerative Scorecard with stakeholders.
• Build and validate “peat-aware” emulators of the JULES‑PEAT model.
• Create a decision layer for ranking rewetting options under uncertainty.

Skills

Quantitative analysis

Machine learning

Uncertainty quantification

Education

Strong quantitative background (e.g. mathematics, statistics, data science, physics)

Job description

A research institute is seeking candidates for a project focused on developing AI models to optimize peatland regeneration in Scotland's Flow Country. Successful candidates will co-design tools with stakeholders, build emulators, and create decision-making frameworks. Ideal applicants will have a strong quantitative background in mathematics, statistics, or related fields, with a keen interest in machine learning and environmental applications. Applications are open until 9 January 2026. Contact Dr. Eric Hall for inquiries.