The University of Southampton

Industry-Sponsored Studentships

We work with all our CDT students to identify the right research project for them. You will have many opportunities to meet potential supervisors and industry partners thinking of co-sponsoring our students in the first few months of the iPhD programme. The CDT team will support you in selecting your research project and in shaping your research ideas.

Many projects are available across all our themes, including research focussing on hardware/software co-design, machine learning, robotics, embedded AI systems, novel hardware for AI, AI and security, and human-AI collaboration.

The projects that our current students are investigating include:

  • Distinguishing audio sources for human-robot collaboration;
  • Energy-efficient hardware for intelligent ECG monitoring;
  • Energy-efficient DNNs for Inference at the EDGE;
  • Decentralised AI for Situational Awareness;
  • Progressive Intelligence for Resource-constrained Artificial Neural Hardware Architectures; and
  • Few Data, Low Resource – Exploiting Invariance in Deep Learning Methods

There are a number of specific projects that we have confirmed industry co-funding for, and these are highlighted here. 

 

Hardware accelerators for space applications -

The aim of this PhD studentship is to develop radiation-hard AI hardware accelerators that can be used for delivering advanced computing capabilities in inaccessible and harsh environments (e.g. space).

AI hardware accelerators

The aim of this PhD studentship is to develop ultra-low power AI hardware accelerators that can be used for delivering advanced computing capabilities at the edge.

Hardware for symbolic processing AI

The aim of this PhD studentship is to develop AI hardware accelerators that can be used for delivering computing capabilities beyond statistical learning, entailing computation at symbol-level.

Hardware accelerators for neuromorphic sensing

The aim of this PhD studentship is to develop ultra-low power sensing processing modules that handle spiking inputs sourced by neuromorphic sensors (e.g. silicon retinas, e-nose, etc).

Hardware for optics-based memories

This PhD studentship aims at exploring advanced materials and novel nanopatterning techniques to develop the next generation of photoelectric memories.