Important: Students should not restrict their search for a supervisor to those listed below. Use other sources of information on research groups to find out about possible supervisors. Most UROP research experiences are obtained with staff who do not advertise their availability. However, please also take note of the list of non-participating staff.

UROP Opportunities in the Faculty of Engineering
Title of UROP Opportunity (Research Experience) & DetailsExperience required (if any)Contact Details and any further Information

Digital ElectronicsThe development of tools and techniques to help automate the design of digital circuits from high level specifications. The implementation of algorithms in reconfigurable hardware or combined hardware/software

An interest and skills in both software and hardware (digital).


Prof George Constantinides, Circuits and Systems Research Group, Dept of Electrical Engineering, Room 910, Electrical Engineering Building, South Kensington Campus. Tel: 020 7594 6299 Email:

Non-Destructive Testing: Components and structures in safety-critical applications must be tested before service and at intervals during their operating life to ensure that there are no defects such as cracks or delaminations present which could cause failure. The tests which are carried out must not damage the component and are, therefore, termed non-destructive. Many parameters which can give information about the integrity of components are measured but there is no universally applicable technique and several areas, such as adhesive joints, are not adequately covered by existing test methods.

Current research is investigating the potential of sonic vibration and ultrasonic measurements for the detection of defects. Opportunities are available in these areas.

  Professor Mike Lowe, Department of Mechanical Engineering, Rm 461a, Mechanical Engineering Building, South Kensington Campus. Tel: 020 759 47071; Email:

[NEW : 29 January, 2020] : Title of Research Experience (Project Title): Towards Computation and Space Efficient Super-Resolution: Algorithms based on Structured Matrices

Project description: Super-Resolution is a signal processing approach promising a resolution much finer than the empirical criterion known as Rayleigh limit. The core idea of super-resolution is to estimate the parameters of sparse signal components where the sparsity is defined in a continuous space. This notion of sparsity over continuum avoids self-interference introduced by the leakage effect of discrete griding and large computational complexity brought by ultra-fine grids. Moreover, recent advantages of super-resolution show that certain super-resolution problems can be formulated as finite dimensional convex optimization problems and hence can be provably solved in polynomial time. 

This project targets at tailored algorithms that improve the efficiency of super-resolution in orders of magnitude. There are two technical elements essential to modern super-resolution techniques: one is structured matrices including Hankel and Toeplitz matrices and the other is decompositions of these structured matrices. Literature has shown, for example, that the computational complexity of eigenvalue decomposition or singular value decomposition of Hankel matrix can be reduced from O(N^3) to O(N log(N)) if one switch the general solver to a solver specifically designed for Hankel matrices. This huge improvement of efficiency is paramount in practice.

While efficient algorithms for Hankel/Toeplitz matrices have been well studied before, there is no work yet in the literature to explore their potentials to super-resolution induced optimization problems. It is well known that the standard solver of super-resolution starts to become impractical when the dimension of the samples goes beyond hundreds. If successful, this project will provide practical tools to handle super-resolution problems of much larger dimension.

This project helps student obtain deep understanding of linear algebra and numerical computations, acquire practical skills of numerical programming and managing related projects, gain much insight in time series related data processing, earn experiences in optimization techniques, and catch glimpses of modern super-resolution theory and techniques.

Skills and experience required: Linear algebra including eigenvalue decomposition Contact details: Dr Wei Dai, Room 811, Dept of Electrical and Electronic Engineering, Faculty of Engineering, South Kensington Campus. Email:


Added: 21 February, 2020

UROPs in Centre for Doctoral Training in Nuclear Energy Futures

Various placements across the Faculty of Engineering covering projects across nuclear science and engineering

The skills and experience required will depend on the project/UROP being offered  

Please view the information and contact details, including the FAQs and the listed opportunities at:

UROP Opportunities in the Faculty of Engineering
UROP Opportunities in the Faculty of Engineering