CSC PhD Studentships in Electronic Engineering and Computer Science

Supervisor: Dr Ignacio Castro

Second Supervisor: Dr Richard Clegg

Social networks are ubiquitous in modern life, they are a fun way to interact with friends as well as read news and pass on information. “Echo chambers”, are a well-known problem of social networks that reinforce the opinions of its users and lead to polarisation. 

This project will look at the emergence and evolution of echo chambers and in particular at how new “ideas” and “topics” emerge and evolve and circulate between different echo chambers. This is a data-driven project that could be approached from a computational social-science and/or mathematical angle (depending on student preference). The project might use Machine Learning, Social Network Analysis, and/or Natural Language Processing techniques. There is flexibility to shape the exact nature of the project around this core idea and the student will work with the supervisors to agree on the exact goals and methods.

Supervisor: Dr SaeJune Park

Supervisor: Dr Shady Gadoue

Multiphase electric drive powertrains are becoming increasingly popular in marine and automotive applications. The multiphase drive powertrain system has many features including increased reliability and efficiency, fault-tolerant operation, and reduced converter rating. Online battery state estimation techniques are required to continuously monitor the battery state of health and state of charge during operation and further predict failure and ageing of the battery system. No much research has been carried out on online state estimation of battery systems in those automotive and marine propulsion applications employing multi-phase drive systems.

The use of existing estimation techniques, based on the conventional drive powertrain system, affects the system performance including induced unwanted torque ripples. Due to the features of extra degrees of freedom offered by the proposed multi-phase configuration, it is expected to reduce the impact on the system performance. This project will investigate the use of the multi-phase power converter modulation to produce the battery impedance graphs online and, in conjunction with predictive control techniques, ageing models for the battery system will be developed, all achieved with minimal effect on the powertrain drive performance. Another feature of the proposed technique is the utilisation of the already-existing power converter for the production of the perturbation signal required for the prediction; thus reducing the cost and size of the estimation process, a feature greatly required in on-board electric automotive and marine propulsion systems. 

Supervisor: Prof Sean Gong

Object recognition in computer vision requires a model to perform two simultaneous tasks: localisation and classification. Modern deep learning object recognition models are poor on small and/or occluded objects. Robust object recognition is challenging to data-centric machine learning. Data augmentation is a technique for improving model learning under unknown biased data sampling. For objects with small training data of unknown bias in sampling, distribution expansion has been shown to be effective. This research will investigate self-learning methods with distribution expansion for learning unbiased object recognition.

For more information, please contact Prof Gong by email, and visit http://www.eecs.qmul.ac.uk/~sgg/.

The student will be based in the Computer Vision Group at the School of Electronic Engineering and Computer Science.

References:

1. J. Huang, S. Gong. “Deep Clustering by Semantic Contrastive Learning”. In Proc. British Machine Vision Conference, London, November 2022.

2. Q. Li, J. Huang, J. Hu, S. Gong. “Feature-Distribution Perturbation and Calibration for Generalized Person ReID”. arXiv preprint arXiv:2205.11197, May 2022.

3. P. Li, D. Li, W. Li, S. Gong, Y. Fu, T. Hospedales. “A Simple Feature Augmentation for Domain Generalisation”. In Proc. IEEE International Conference on Computer Vision, Montreal, Canada, October 2021.

Supervisor: Dr Yuanwei Liu

Supervisor: Dr Yuanwei Liu

Supervisor: Dr Mahdieh (Maddie) Sadabadi

The increasing deployment of power electronics converters in safety-critical applications (such as microgrids and electric vehicle charging stations) demands robustness against system faults and deteriorations. The complexity of the dynamics of power electronics converters, along with the major investment in process data collections, have highlighted the need for data-driven approaches for diagnosis and prognostics, as a fundamental tool for predictive maintenance mechanisms in such systems.

The main aim of this project is to develop a health-aware control system for power electronics converters that integrates the information provided by a data-driven prognosis module about converters’ health to accordingly adapt the controller, extending the life of the power electronics converters. The developed health-aware control technologies will be verified by real-time digital simulators and hardware-in-the-loop experimental testbeds. 

Prospective applicants should have a first-class degree and/or Master’s degree (Distinction) or be close to completion in Control Engineering/Electrical Engineering or related subjects. In addition, you should also have a strong background in control systems. Familiarity with machine learning and power electronics is a plus.