Current research degree projects
Explore our current postgraduate research degree and PhD opportunities.
Explore our current postgraduate research degree and PhD opportunities.
The University of Southampton is expanding its PhD research in the area of Quantum Technology Engineering. In addition to the research project outlined below you will receive substantial training in scientific, technical, and commercial skills.Colloidal quantum dots are semiconductor nanocrystals that sit in between molecular and bulk materials. Their small size (typically <10 nm in diameter) are comparable to the material’s Bohr radius, leading to quantum confinement of excitons and size- and composition-tunable optoelectronic properties. Compared to other quantum-confined nanostructures (e.g. epitaxial quantum dots, wires or wells) they have the advantage of being solution-processable, which makes them well suited for mass production of devices.
The University of Southampton is expanding its PhD research in the area of Quantum Technology Engineering. In addition to the research project outlined below you will receive substantial training in scientific, technical, and commercial skills.
Around 70% of Europe’s offshore wind turbines are installed in the North Sea. Chalk, a calcareous weak rock, is widely present in this area. Driven piles are currently the preferred foundation system for these structures, but pile design entails a high level of uncertainty. Our recent collaborative work on helical (‘screw’) piles has indicated their potential suitability for these highly challenging offshore applications. The relative ease of installation of screw piles at depth may offer a much more convenient method for anchoring wind turbine foundation systems. This can potentially reduce the cost of offshore renewables, accelerate the rate of infrastructure deployment, and significantly contribute towards meeting energy decarbonisation targets.
Applications are invited for a fully funded PhD position on the investigation of novel design methods to improve the resilience of submerged infrastructure (i.e. bridge piers, wind turbines) subject to hydrodynamic action (e.g. currents and waves).Many critical submerged infrastructures are exposed to increased risk due to climate-induced changes in environment conditions, such as increased river current or wave action. In order to design the next generation of submerged infrastructure that are sustainable and both cost and carbon efficient, new design methods are required.
We are seekign an outstanding chemistry student with an interest in chemical biology to work on a project to develop cyclic peptide inhibitors of a protein-protein interaction that is heavily implicated in the development and growth of tumours.Mutant RAS proteins drive numerous cancers, with their inhibtion shown to have therapeutic potential in several tumour types. The majority of RAS inhibitors currently in development target the G12C KRAS mutant through a covalent inhibition mechanism, which limits their use to a small subset of cancers. The Tavassoli lab has used an in-house genetically-encoded library of cyclic peptides to identify several cyclic peptides that inhibit the interaction of RAS proteins with their affectors, and therefore inhibit downstream signalling in cancer cells.
Endofullerenes are a new class of materials in which small molecules or atoms are completely enclosed in carbon cages. The encapsulated atoms or molecules behave like “particles in a box” which have their own set of quantum energy levels. In this project, you will be trained in the theory and practice of inelastic neutron scattering (INS), in order to study the quantum properties of these remarkable new materials.
There is an ongoing transformation in engineering autonomous systems that aim to achieve complex objectives with limited human intervention in applications such as robotics, self-driving cars, and industrial systems. While the design of autonomous systems has typically relied on pre-defined models, the desire to operate in complex, unknown, or varying conditions implies that models of the system and the operating environment may not be always available. As a result, machine learning and data-driven approaches are on the rise and have the potential for impact in autonomous systems. However, embedding machine learning in autonomous systems is facing significant challenges in terms of safety, robustness, and resource efficiency.