Current research degree projects
Explore our current postgraduate research degree and PhD opportunities.
Explore our current postgraduate research degree and PhD opportunities.
This project will reimagine gel electrophoresis into an innovative engineered microfluidic system, tailored to match the latest long-read sequencing methods for genomic medicine and health data science. By shrinking a decades-old method, that remains essential to DNA analysis, we will enable faster, higher quality genomic analysis.
Traditional analytical methods are often overwhelmed by complex samples. In partnership with your supervisors and LECO Instruments UK Ltd, you'll develop new analytical methods, on world-leading comprehensive gas chromatography-mass spectrometry (GCxGC-MS) instrumentation, providing greater insights and helping to address challenges in a range of academic and industrial sectors.
This project will harness MXenes, cutting-edge two-dimensional materials, to create powerful new technologies for detecting and removing these pollutants, with the goal of developing MXene-based prototypes for use in real-life conditions.
In this project, you will explore the design of many-body quantum echoes that amplify structurally sensitive data for quantum computation. By combining controlled quantum experiments with quantum algorithmic methods, you will gain hands-on experience applying fundamental quantum techniques to real-world problems.
This PhD project will develop metasurface-enabled intelligent optical sensing for rapid, accurate identification of miniature features in endoscopy. Building on recent funding from the Engineering and Physical Sciences Research Council (EPSRC) and the Leverhulme Trust, this PhD combines advanced nanofabrication, machine-learning-driven optical design, and close collaboration with University Hospital Southampton, Nanyang Technological University (NTU), Singapore, and the Massachusetts Institute of Technology (MIT).
CPUs with hundreds of cores are expected to take over the computing industry from embedded AI devices to servers. While hardware prediction algorithms like data prefetchers greatly improve instruction-level parallelism, they often focus on single-thread performance. This project will explore novel prediction algorithms for manycores and their theoretical limits.