Primary Supervisor - Prof Kate Kemsley

Scientific Background

Deforestation is a major global issue, destroying biodiversity and accelerating climate change by removing vital carbon sinks. The newly introduced EU Deforestation Regulations aim to reduce the environmental impact of ‘Forest Risk Commodities’ (FRCs) such as soy, palm oil and coffee. Land-use changes, like forest clearing and burning that cause a nutrient surge, may leave characteristic trace chemical or isotopic signatures in these crops, suggesting a potential route to testing and verifying FRC origins, and supporting deforestation-free supply chains.

Research Methodology & Training

This project will explore the use of advanced analytical methodologies for detecting and identifying chemical and isotopic markers linked to deforestation in FRCs. You will receive training in instrumental techniques, including stable isotope analysis using specialised equipment within the UEA Science Analytical 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 skills in data science, including multivariate analysis, machine learning and AI, to interpret complex datasets 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 Schools of Chemistry, Pharmacy & Pharmacology and of Environmental Sciences, gaining experience in both laboratory and computational approaches. You will benefit from two 3-months secondments to the project’s industry supporter, Fera Science (Sand Hutton, York), where you will have access to complementary instrumentation and expertise within the Food Authenticity team.

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

Person Specification

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

Informal enquiries concerning the project are welcomed by the primary supervisor.

Entry Requirements

At least UK equivalence Bachelors (Honours) 2:1. English Language requirement (Faculty of Science equivalent: IELTS 6.5 overall, 6 in each category).

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

Mode of Study

Full-time

Start Date

1 October 2026

Funding Information

Note – use this field to provide related salary info e.g. plus London Allowance

ARIES studentships are subject to UKRI terms and conditions . Successful candidates who meet UKRI’s eligibility criteria will be awarded a fully-funded studentship, which covers fees, maintenance stipend (£20,780 p.a. for 2025/26) and a research training and support grant (RTSG). A limited number of studentships are available for international applicants, with the difference between 'home' and 'international' fees being waived by the registering university. Please note, however, that ARIES funding does not cover additional costs associated with relocation to, and living in, the UK, such as visa costs or the health surcharge.