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Applications are invited for a self-funded, 3-year full-time or 6-year part time PhD project.

The PhD will be based in the School of Computing and will be supervised by Dr Farzad Arabikhan, Dr Keiron Roberts, and Professor Mohamed Bader.

Litter pollution presents a persistent and visible challenge in both urban and coastal environments, impacting public health, biodiversity, and local economies. Yet, policy responses often lack the data granularity and analytical tools necessary to support targeted interventions. This PhD project aims to bridge that gap by integrating Artificial Intelligence (AI), spatial analytics, and real-time litter data to support evidence-based policy development and environmental impact assessments.

The project will focus on developing a scalable AI-based platform that ingests real-time data from a range of sources—including smart bins, CCTV, drones, and citizen science apps. Using advanced machine learning and spatial clustering algorithms, the platform will detect and analyse litter accumulation patterns across time and space.

Key components of the research will include:

1. Data Integration and Preprocessing: Aggregating real-time data from heterogeneous sources and ensuring quality, consistency, and temporal-spatial alignment.
    
2. Machine Learning and AI Modelling: Training supervised and unsupervised models to predict litter accumulation, identify behavioural patterns, and forecast high-risk zones under different environmental or social conditions.
    
3. Spatial Analysis and Clustering: Applying methods such as DBSCAN or k-means to detect spatial clusters of littering behaviour, link them with land use and socio-demographic factors, and visualise insights via geospatial dashboards.
    
4. Policy Impact Assessment: Evaluating the effectiveness of past and ongoing interventions (e.g., public bin placement, awareness campaigns), and modelling the potential impact of new policy options.
    

This interdisciplinary project lies at the intersection of environmental science, AI, urban planning, and behavioural policy. It will engage with stakeholders such as local councils, environmental charities, and public health organisations to ensure that outcomes are relevant, applicable, and impactful.

Ideal for candidates who want to apply data science for real-world environmental and societal benefit, this PhD offers access to rich datasets, expert supervision, and a strong pathway to policy influence.

Fees and funding

Visit the research subject area page for fees and funding information for this project.

Funding availability: Self-funded PhD students only. 

PhD full-time and part-time courses are eligible for the UK  (UK and EU students only).

Bench fees

Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.

How to apply

We’d encourage you to contact Dr Farzad Arabikhan (farzad.arabikhan@port.ac.uk) to discuss your interest before you apply, quoting the project code.

When you are ready to apply, please follow the 'Apply now' link on the Computing PhD subject area page and select the link for the relevant intake. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process. 

When applying please quote project code: CMP10121026