AI-M Coast: AI-Modelling of saltmarsh and seagrass vegetation for coastal protection

About the project

About the Project Overview 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. Experimental and numerical modelling will investigate how vegetation reduces wave energy and causes flow resistance. 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. The student will join a supportive and collaborative research environment with opportunities for training, interdisciplinary collaboration, publications, and professional development in both environmental science and AI. 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 Applying transfer learning techniques to improve model scalability and applicability across multiple restoration sites and environmental settings. 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 but is not essential. We are seeking a candidate who can demonstrate strong analytical and problem-solving skills, an interest in interdisciplinary research, and the ability to work collaboratively across computing and environmental science disciplines. 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. How to apply The School of Computing and Mathematics is inviting applications for eight independent doctoral projects, with five funded Doctoral Teaching Assistant positions available. All applications will go through an initial review stage conducted by the project supervisors. Candidates selected from this stage will be invited to an interview, where final funding decisions will be made based on performance across all projects. 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 Application link : PhD Computing and Digital Technology — six years part-time

Research areas