Honours and Masters project

The world’s energy markets are transforming, and more renewable energy is integrated into the electric energy market. The intermittent renewable supply leads to unexpected demand-supply mismatches and results in highly fluctuating energy prices. Energy arbitrage aims to strategically operate energy devices to leverage the temporal price spread to smooth out the price differences in the market, which also generates some revenue.

The rapid growth of electric vehicles (EVs) is transforming the transportation systems worldwide. Both EV fleets and private EVs are emerging as a cleaner and more sustainable component of urban mobility, forming an effective way to solve environmental problems and reduce commute costs in future smart cities. Due to the complex spatiotemporal behaviors of passengers and their travel patterns, the unmanaged electric charging demand from EVs may significantly impact the existing transportation and electrical power infrastructure.

Thanks to the widespread deployment of smart meters, high volumes of residential load data have been collected and made available to both consumers and utility companies. Smart meter data open up tremendous opportunities, and various analytical techniques have been developed to analyse smart meter data using machine learning. This project will provide a new angle toward energy data analytics and aims to discover the consumption patterns, lifestyle, and behavioural changes of consumers.

Studies using computed tomography (CT), particularly high-resolution CT and quantitative CT have become a crucial non-invasive method for examining airway thickness and the structural changes known as airway remodeling in chronic respiratory diseases such as asthma. Bronchial thermoplasty is a treatment option for patients with severe asthma and works by applying heat energy to reduce the amount of excess airway smooth muscle, which is often abnormally thickened in patients with asthma.

Modern transport and logistics rely on efficient routing to ensure that goods are delivered on time and at minimal cost. Determining the optimal order in which vehicles visit a set of customers is a fundamental challenge in mathematics and computer science. Despite decades of research, this problem remains computationally difficult. To achieve reasonable cost-effectiveness in industry-scale applications, the best-performing methods rely on heuristic and suboptimal solvers. This project will develop new suboptimal algorithms for tackling large instances relevant to practice.

Shortest path algorithms are used to advise travelers on the best route from an origin to destination across various modes including car and public transport. One important variant of the standard shortest path problem is the inclusion of resource constraints, where a user has a limited budget in cost, time, carrying capacity, etc. and needs to find a shortest path within this budget. Unlike the regular shortest path problem, the resource-constrained shortest path problem is theoretically proven to be NP-hard, making its computation difficult.

An "agent" or "client" in the context of mobile forensics is the program deployed using an exploit to a mobile device to facilitate extraction of the data from the phone. Some agents can also do other functions like passcode brute-force attacks.

In today's digital landscape, cyberattacks are increasingly impacting organisations by disrupting critical services and compromising sensitive data. As these attacks grow in volume and complexity, security teams are increasingly challenged to safeguard sensitive data and maintain operational continuity. Manual efforts of security assessment often led to inconsistent and delayed results, high operational costs, and increased window of opportunity for potential attackers. To effectively mitigate these risks, there is a pressing need for automated security assessment.

Providing timely, individualised feedback is a persistent challenge in large-scale computing units. This project investigates how Generative AI models can automatically produce pedagogically aligned, rubric-based feedback on student submissions. A prototype system will interface with an LLM API (e.g., OpenAI GPT) and generate structured feedback, which will be evaluated for accuracy, usefulness, and tone against educator benchmarks.

Quantum algorithms such as Grover’s Search promise quadratic speed-ups over classical search but are sensitive to hardware noise. This project will use Qiskit Aer to model realistic noise channels (decoherence, gate and readout errors) and evaluate their impact on algorithmic performance. By varying circuit depth, qubit count, and noise parameters, the student will identify conditions under which quantum advantage remains achievable and investigate possible error-mitigation strategies.