PhD Studentship: Next-Generation Marine Ecosystem Indicators: Machine Learning for Smarter Marine Spatial Planning in a Changing Climate

University of East Anglia - School of Environmental Sciences

Primary Supervisor -Dr Neda Trifonova

Background

The UK marine environment is a complex, high-demand space. It provides vital services, including food provision through fisheries and aquaculture, and energy security via offshore wind generation. Balancing these uses while preserving biodiversity and protecting marine ecosystem health is increasingly challenging. As human pressures and climate change accelerate, we urgently need smart, evidence-based tools to plan, manage, and protect our marine ecosystems.

At the forefront of this innovation is machine learning. Its ability to process complex, multidimensional datasets is transforming marine ecology and redefining how we detect and respond to ecosystem change.

Methodology

This PhD will place you at the forefront of this emerging field. You will address a key challenge: assessing what makes a reliable measurable indicator of change in marine ecosystems (e.g., shift in species populations) and interpreting what these indicators reveal about ecosystem health.

You will develop and apply cutting-edge machine-learning techniques to identify the most informative indicators of ecosystem change and use them to build dynamic Bayesian network (DBN) ecosystem models. The project’s key objectives are to: 1) Identify critical indicators relating to ecosystem health and resilience; 2) Incorporate indicators into DBN models to simulate how ecosystems respond to future climate and human use scenarios; 3) Investigate the merits and drawbacks of DBN models in different geographic locations and over varying timescales; 4) Translate DBN model outputs into policy-relevant insights supporting the design of effective Marine Protected Areas (MPAs) and informed Marine Spatial Planning.

Training

Based at the University of East Anglia, you will gain expertise in marine ecosystem modelling using both field-based and modelled physical and biological data. Collaboration with Centre for the Environment, Fisheries and Aquaculture Science will provide exposure to how science informs UK policy on marine biodiversity and MPAs design. You will gain sea-going field experience and be trained in a range of state-of-the-art instruments aboard the RV CEFAS Endeavour.

Person Specification

We seek an enthusiastic individual who is interested in marine ecology, computing, with some prior experience in programming and data handling, eager to communicate findings to wider stakeholders and help shape the UK’s marine conservation strategies and the sustainable use of the marine environment.

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: environmental, marine or mathematical sciences (or similar)

Mode of Study

Full-time

Start Date

1 October 2026

Funding Information

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.