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.
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.
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.
Conditional Generative Adversarial Networks (cGANs) have gained notoriety in the media for their ability to create so-called “deep fakes” but their power can also be harnessed to provide predictions of optimal engineering designs. Such techniques offer the potential to significantly reduce design times by providing engineers with an instant solution to a design problem.