Machine Learning approaches to estuarine and coastal ecosystem health Ref: 4784

at University of Exeter
Published May 5, 2023
Location Exeter, United Kingdom
Category Machine Learning  
Job Type Scholarship  


About the award


Professor Peter Challenor, Department of Mathematics, University of Exeter

Professor Daniel Williamson, Department of Mathematics, University of Exeter


Centre for Doctoral Training in Environmental Intelligence, Streatham Campus, Exeter

The University of Exeter’s Centre for Doctoral Training in Environmental Intelligence, in partnership with the Plymouth Marine Lab, is inviting applications for a fully-funded PhD studentship to commence in September 2023. The successful applicant will join the UKRI CDT in Environmental Intelligence, and will be included in CDT cohort building and training activities.  The successful applicant will work on the below project under the supervision of Peter Challenor and Daniel Williamson (University of Exeter), with additional supervision and support from Plymouth Marine Lab.


Project Description:
Advances in hydrodynamic and biogeochemical modelling are dramatically increasing our ability to model marine coastal areas at unprecedented detail. Such operational models can provide real-time information about coastal conditions like currents, suspended sediment concentrations and biogeochemical parameters such as chlorophyll-a concentrations, plankton biomass and nutrients. These models, while highly sophisticated, can still suffer from structural uncertainty, parameter uncertainty or initial condition uncertainty which can limit their uptake. Additionally, their high computational cost also restricts their use in scenario and/or ensemble mode. On the other hand, our ability to efficiently monitor the vast number of ecosystem processes is still very limited. The ability to combine scarce data with “imperfect” models promises to improve our understanding of marine ecosystems and will play a significant role in our ability to monitor our coasts to fulfil environmental regulations and UK’s implementation of climate legislation to reduce carbon emissions.

We propose to develop new approaches of data blending that are operational feasible and suitable for exploring a wide range of policy and climate scenarios as well as contribute to regular monitoring of the health of our coast. The candidate will develop light weight emulators for the Tamar Estuary to act as an interface for an eventual Digital Twin of the biogeochemistry of the area. The emulators will be designed to predict from potentially observable inputs (remote sensing, distributed network of in-water sensors) key indicators of estuarine conditions, such as nutrients, biological production, CO2 air-sea fluxes and bottom oxygen concentrations. Furthermore, the emulators will determine the requirements on the satellite (low-resolution) as well as in situ observing networks (variables and locations) that enable skilled prediction of those environmental indicators. Finally, these emulators should be capable of addressing what-if type scenarios related to 1) land use changes by its impacts on nutrients, 2) population growth by its impacts on nutrients via waste waters, 3) the impact of climate change on estuarine productivity and hypoxia through increase of winter storms and river flows, as well as through increased thermal stratification and sea level rise.


About the UKRI Centre for Doctoral Training in Environmental Intelligence
Our changing environment presents a series of inter-related challenges that will affect everyone’s future health, safety and prosperity. Environmental Intelligence (EI) is the integration of environmental and sustainability research with data science, artificial intelligence and cutting-edge digital technologies to provide the meaningful insight to address these challenges and mitigate the effects of environmental change.  One of the 16 UKRI AI CDTs launched in 2019, the CDT in Environmental Intelligence provides an interdisciplinary training programme for students covering the range of skills required to become a leader in EI:
• the computational skills required to analyse data from a wide variety of sources;
• expertise in environmental challenges;
• an understanding of the governance, ethics and the potential societal impacts of collecting, mining, sharing and interpreting data, together with the ability to communicate and engage with a diverse range of stakeholders.

The CDT cohort works and learns together, bringing knowledge, skills, and interests from a range of academic disciplines relevant to EI.  CDT students undertake training and professional development as a cohort, and regularly participate in seminars, symposia, and partner engagement activities including the annual CDT Environmental Intelligence Grand Challenge.  As part of the research community at the University of Exeter, CDT students benefit from networking with colleagues in the Institute for Data Science and Artificial Intelligence; the Global Systems Institute; and the Environment and Sustainability Institute.


Entry requirements

This award provides annual funding to cover Home tuition fees and a tax-free stipend.  For students who pay Home tuition fees the award will cover the tuition fees in full, plus at least £17,668 per year tax-free stipend.  Students who pay international tuition fees are eligible to apply, but should note that the award will not provide the international element of the tuition fees (approx £15,000 per annum).
International applicants need to be aware that you will have to cover the cost of your student visa, healthcare surcharge and other costs of moving to the UK to do a PhD.
The conditions for eligibility of home fees status are complex and you will need to seek advice if you have moved to or from the UK (or Republic of Ireland) within the past 3 years or have applied for settled status under the EU Settlement Scheme.


Applicants for this studentship must have

• obtained prior to the start of the PhD, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK, in an appropriate area of science or technology, i.e. environmental, geographical, mathematical, or computer science study programme.
• a keen interest in environmental research
• sound numerical and computational experience
• If English is not your first language you will need to meet the required level (Profile A)  as per our guidance at


How to apply

In the application process you will be asked to upload several documents .
• CV
• Letter of application (outlining your academic interests and expertise, prior research experience and reasons for wishing to undertake the project).
• Transcript(s) giving full details of subjects studied and grades/marks obtained (this should be an interim transcript if you are still studying)
• Names of two referees familiar with your academic work. You are not required to obtain references yourself. We will request references directly from your referees if you are shortlisted.
• If you are not a national of a majority English-speaking country you will need to submit evidence of your proficiency in English.
The closing date for applications is midnight on 31 May 2023. 


Application deadline: 31 May 2023
Value: Home fees plus annual tax-free stipend of at least £18,622 in year 1. £10,000 (approx.) project budget for research and training.
Duration of award: 4 years