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.
Research projects in Information Technology
Displaying 1 - 10 of 221 projects.
Multimodal AI and Machine Learning for Diabetes-Related Complications: Integrating Clinical Prediction, Decision Support and Longitudinal Risk Forecasting for Diabetic Foot Disease and Lower-Limb Amputation
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
Advanced AI and Machine Learning for Endometriosis and Polycystic Ovary Syndrome: Predictive Modelling, Clinical Decision Support and Longitudinal Risk Forecasting for Personalised Healthcare
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
Explainable Artificial Intelligence and Machine Learning for Early Detection, Risk Stratification and Prognostic Prediction of Colorectal Cancer: Advancing Personalised Clinical Decision Support
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
Advanced Artificial Intelligence and Machine Learning for Cardiovascular Disease: Multimodal Prediction, Risk Stratification and Prognostic Modelling for Personalised Healthcare
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
Early Prediction and Risk Stratification of Ovarian Cancer Using Explainable AI and Machine Learning for Improved Survival Outcomes.
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.
Unlocking the Potential of Electric Vehicles as Future Energy Storage Solutions
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.
Reimagining Public Transport with Diverse Autonomous Vehicles
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.
TechToys co-designed with children with cerebral palsy: Reimagining play
Play is fundamental and one of the main methods children learn skills, gain independence, and practice daily tasks that lead into adulthood. TechToy Library is an innovative, co-designed initiative creating toys specifically designed for children with cerebral palsy, empowering them to play, learn, and connect. Through collaboration with families, allied health professionals, and people with lived experience, the project develops inclusive toys using cutting-edge technologies like augmented reality and 3D customisation.
The Conversational Refreshable Tactile Display: Multimodal AI for people who are blind or have low vision
For decades, the primary way people who are blind or have low vision (BLV) interact with computers has been through screen readers. These linearise an application or document interface, presenting content as synthetic speech or on a single-line braille display, which the user navigates using keyboard shortcuts or touch gestures on a smartphone. Screen readers have a major limitation: they cannot show graphics. Instead, they rely on alternative text descriptions, which are often incomplete or inadequate.