Research projects in Information Technology

Science depends on institutions. Journals decide what is published, reviewers assess work produced by their peers, funders choose which questions receive support, and universities reward some kinds of contribution more than others. These arrangements shape what scientists study and how carefully they study it.

Cooperation is difficult whenever individual incentives conflict with a shared goal. This problem appears in human societies, biological systems and groups of artificial agents. It also lies behind practical questions about collective action: whether communities manage shared resources, whether autonomous systems coordinate safely, and whether agents trained separately can work together when they meet.

This project studies how cooperation emerges, persists and fails among intelligent agents. Possible questions include:

Conservation management needs reliable information about how many animals a population holds and whether that number is changing over time. Yet for most threatened species such estimates are often unavailable or imprecise, either because the species is cryptic and hard to detect by visual monitoring or because current methods struggle to separate individuals from one another. Passive acoustic monitoring, i.e.

Background

Diabetes can lead to serious complications, including peripheral neuropathy, peripheral arterial disease, diabetic foot ulcers and lower-limb amputation. Early identification of patients at increased risk is important for timely intervention, preventive care and improved clinical outcomes. The increasing availability of electronic health records, clinical measurements and longitudinal patient data provides opportunities for advanced Machine Learning (ML) approaches.

Research Aim

Background

Endometriosis and Polycystic Ovary Syndrome (PCOS) are complex conditions with diverse clinical presentations and potentially long-term health impacts. Variability in symptoms and disease progression can make early identification and risk assessment challenging. The increasing availability of clinical, demographic, hormonal, metabolic and longitudinal health data provides opportunities for Artificial Intelligence (AI) and Machine Learning (ML) to support earlier and more personalised healthcare.

Research Aim

Background

Colorectal cancer is a major health challenge, and early detection and accurate risk assessment are important for improving clinical outcomes. The increasing availability of clinical, demographic, lifestyle, laboratory, imaging and longitudinal health data provides opportunities to apply Artificial Intelligence (AI) and Machine Learning (ML) to colorectal cancer research.

Research Aim

Background

Cardiovascular disease is a major global health challenge, and early identification of individuals at increased risk is important for prevention and timely clinical intervention. The increasing availability of electronic health records, clinical measurements, medical imaging and longitudinal health data provides opportunities for Artificial Intelligence (AI) and Machine Learning (ML) to improve cardiovascular risk assessment and prediction.

Research Aim

1. Research Objectives & Clinical Problem Late-stage diagnosis remains the leading cause of high mortality in ovarian cancer, as early stages are often asymptomatic or present with non-specific abdominal symptoms. Standalone clinical indicators like CA125 or traditional screening algorithms (e.g., ROMA) frequently produce ambiguous results or lack sufficient sensitivity for early detection.

This PhD project will develop intelligent, data-driven methods that enable electric vehicles (EVs) to serve as mobile energy storage while remaining readily available for transportation. The research will investigate how EV travel, routing, charging and discharging decisions can be jointly optimised using real-time and predicted information about travel demand, traffic conditions, renewable energy availability, electricity prices, charging infrastructure and battery status.

This PhD project will develop intelligent, data-driven methods for integrating diverse autonomous vehicles, such as buses, shuttles, pods, cars and micromobility vehicles, into public transport networks. The research will investigate how heterogeneous fleets can be dynamically routed, scheduled and repositioned in response to changing passenger demand, traffic conditions and public transport schedules, while accounting for differences in vehicle capacity, speed, energy use and service roles.