Primary Supervisor – Professor Kate Kemsley

Scientific background

Deforestation is a major global issue, destroying biodiversity and accelerating climate change by removing vital carbon sinks. Newly introduced EU deforestation regulations aim to reduce the environmental impact of “forest risk commodities” (FRCs) such as soybeans, palm oil and coffee. Land use changes, such as deforestation and burning that cause eutrophication, may leave distinct chemical or isotopic signatures in these crops, suggesting a potential path to testing and validating FRC assets, and supporting deforestation-free supply chains.

Research and training methodology

This project will explore the use of advanced analytical methodologies to detect and identify chemical and isotopic signatures associated with deforestation in FRCs. You will receive training in instrumental techniques, including stable isotope analysis using specialist equipment within the University of East Anglia’s scientific analysis facilities, as well as high-throughput spectroscopic methods suitable for large-scale sample screening and eventual field deployment. The project will also involve developing your data science skills, including multivariate analysis, machine learning and artificial intelligence, to interpret complex data sets and extract meaningful patterns related to geographic origin and land use history.

You will join a vibrant research community and benefit from interdisciplinary supervision across the Faculties of Chemistry, Pharmacy, Pharmacology and Environmental Sciences, and gain experience in both laboratory and computational approaches. You will benefit from two 3-month secondments to the project backer, Fera Science (Sand Hutton, York), where you will have access to complementary tools and expertise within the Food Authenticity team.

During your PhD, you will have regular opportunities to present your work at academic meetings. You will also be able to develop your broader communication skills through interactions with Fera Science, as well as project collaborative partner World Forest ID and other non-academic stakeholders.

Person specifications

We are looking for an enthusiastic graduate with skills in chemical or environmental analysis and a strong computational background. Previous experience in mathematical or statistical programming is highly desirable.

Informal inquiries related to the project are welcomed by the lead supervisor.

Entry requirements

At least a UK BA (Hons) equivalency of 2:1. English language requirements (Faculty of Science equivalent: IELTS 6.5 overall, 6 in each category).

Acceptable first degree: Chemistry, Earth/Environmental Sciences, Natural Sciences, Artificial Intelligence or related subject.

Study method

Full time

Start date

October 1, 2026

Financing information

Note – Use this field to provide relevant salary information, for example in addition to the London Allowance

ARIES Scholarships are governed by UKRITerms and Conditions. Successful candidates who meet UKRI’s eligibility criteria will be awarded a fully-funded scholarship, which covers fees, a maintenance stipend (£20,780 per annum for 2025/26) and a Research Training and Support Grant (RTSG). A limited number of scholarships are available to international applicants, with the difference between ‘national’ and ‘international’ fees waived by the registered university. However, please note that ARIES funding does not cover additional costs associated with moving to and living in the UK, such as visa costs or additional health fees.