Current research degree projects
Explore our current postgraduate research degree and PhD opportunities.
Explore our current postgraduate research degree and PhD opportunities.
The aim of this project is to design, develop, and translate 3D nanoscale metamaterials for real-world applications. In this work, nanoscale particles can be assembled into macroscopic structures, creating a new class of materials where the desired properties are enhanced and scaled to a device level.
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.
Our 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.
The ability to design new more efficient down conversion systems will be of paramount importance for the quantum computing revolution. Molecular lanthanide cluster complexes comprising multiple Ln ions offer unrivalled control in the internuclear distances and donor-acceptor compositions, thereby allowing more sophisticated and efficient down converting devices to be prepared.
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.