Current research degree projects

Explore our current postgraduate research degree and PhD opportunities.
Explore our current postgraduate research degree and PhD opportunities.
Additive manufacturing enables the fabrication of engineering components with a high degree of geometric complexity. This geometric complexity makes the measurement and inspection of metal AM components very difficult. In this project, You will develop new methods for measuring and inspecting complex AM components.
The main goal of this project is to investigate and design novel electrodes for minimally invasive brain sensing.
This PhD project explores the use of Physics-Informed Neural Networks (PINNs) to solve environmental flow problems, including the 2D Shallow Water Equations. Combining advanced artificial intelligence (AI) with fluid mechanics, the research aims to develop fast, accurate, and robust simulations for applications like flood modelling and water management.
Fully funded PhD investigating novel design methods to improve submerged infrastructure resilience against currents and waves. Utilize world-class hydraulic labs and supercomputing facilities to develop sustainable, carbon-efficient solutions.
天发娱乐棋牌_天发娱乐APP-官网|下载 objective is to develop Nuclear Magnetic Resonance spectroscopy to make it capable of detecting individual quantum spins. This goal will be achieved by developing magnetic lenses to amplify the signal from and out of the spin-hosting materials.
This project investigates novel materials such as twisted 2D materials and complex oxides to develop advanced sensors capable of detecting dynamic processes with ultra-high sensitivity for applications in nanometrology and nanoelectromechanical systems (NEMS).
Quantum systems evolve in time. The pathway which a quantum system follows may be controlled by imposing selection rules on the dynamical evolution. This project involves a combination of theory, numerical simulation, and experiments involving local nuclear magnetic resonance equipment and through international collaborations.
This project explores quantum computing to enhance large-scale stochastic optimisation for energy system planning, addressing uncertainty in renewables. By integrating quantum and classical methods, it aims to solve large-scale models, advancing methodologies and supporting the energy transition.
Current photonic quantum systems suffer from the poor brightness of the single photon sources used as a source for the qubits. The PhD position will explore ways of enhancing light extraction from photon sources and ways to detect meaningful qubit information.
Quantum photonics is key to develop the next generation of quantum technologies. This project will develop a silicon-nitride platform for visible-wavelength quantum photonics.