About this position
Coastal flooding is becoming more frequent and severe due to climate change, rising sea levels, and increased storm intensity. Saltmarsh restoration, widely promoted as a nature-based solution, can reduce wave energy and flood risk while also supporting biodiversity and carbon storage. However, there is limited understanding of how effectively restored saltmarshes provide coastal protection compared with natural systems.
This interdisciplinary project will combine laboratory experiments, numerical modelling, and advanced artificial intelligence (AI) techniques to improve predictions of the coastal protection benefits provided by restored saltmarshes.
The successful PhD candidate will work with laboratory experiments and develop numerical and AI models capable of supporting coastal management and restoration planning. AI models will be used to predict coastal protection performance across a range of environmental conditions.
The project offers access to specialist facilities, including the BioGeomorphology Laboratory flume, field datasets from restored and natural saltmarshes across the UK, and expertise from researchers in coastal ecology, hydrodynamic modelling, AI, and data science.
Objectives
The project aims to develop advanced AI models to improve predictions of the coastal protection benefits provided by the restored saltmarshes and seagrass ecosystems. Specific objectives include:
- Generating high-quality experimental datasets using laboratory flume facility.
- Developing and validating hydrodynamic models to simulate the effect of vegetation in different environmental conditions.
- Designing and evaluating AI models for prediction of effectiveness of coastal protection.
Funding
These are doctoral teaching assistant positions that combine a PhD programme with a university teaching contract. Your time will be split approximately 60% on research and 40% on teaching. This provides excellent preparation for candidates considering an academic career at a university.
The teaching component will typically run over the 22 teaching weeks per year and the 4 assessment weeks. You will help deliver an outstanding student experience by supporting lead academics with classroom and lab teaching and assessment, further building the skills developed within your PhD research programme.
The position is grade 6 with a current salary of £31,236 and includes payment of home PhD tuition fees for the duration of the 6-year award. Home students can apply. Applicants must have the right to work in the UK. We are unable to offer visa sponsorship for this role.
Candidate requirements
Applicants should hold, or expect to obtain, undergraduate and preferably a postgraduate qualification in computer science, artificial intelligence, data science, environmental modelling, coastal engineering, geography, or a related discipline.
Desirable
- Experience in machine learning, deep learning, data analysis, numerical modelling, or scientific programming (such as Python, MATLAB, or R) is desirable.
- Knowledge of hydrodynamic modelling or environmental systems would be advantageous.
How to apply
If you have any questions, contact the principal supervisor, Dr Pavitra Kumar .
To apply you will need to complete the online application form for a part time PhD in Computing & Digital Technology.
Please complete the Doctoral Project Applicant Form , and include your CV and a covering letter to demonstrate how your skills and experience map to the aims and objectives of the project, the area of research and why you see this area as being of importance and interest.
Please upload these documents in the supporting documents section of the University’s Admissions Portal or send them to the PGR Admissions team at pgradmissions@mmu.ac.uk .
Please quote the reference: SciEng-DTA Jan 2027-PK-AIM Coast
£31,236 per annum
Funding
GBP 31,236 per year
How to apply
- Read the full advert on the source site — it carries the authoritative terms.
- Email the contact below with your CV and a short, specific message. See Emailing Professors.
- Prepare your SOP, CV, transcripts and referees before the deadline.
- Apply through the university's own portal. Never pay a fee to a third party.