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Pulse Shape Analysis for Radiation Detection with Machine Learning

Pulse Shape Analysis is an electrical signal processing method which allows the identification of radiation particles or their interaction properties based on the shape of the electric signal produced through the interaction of the radiation with a detection sensor. The method has multiple applications ranging from fundamental science such as nuclear and particle physics, to industrial and medical applications such as non-destructive analysis or medical and industrial imaging.

Advances in Machine Learning makes possible the development of performant algorithms for Pulse Shape Analysis using pattern recognition methods. This project aims to develop such sophisticated algorithms for silicon-based radiation sensors.

The student working on this project will have the opportunity to develop knowledge and skills related to electrical signal processing, radiation sensors, and machine learning. Such combined abilities will result in a high employability level having the flexibility of applying them to a diverse range of domains.

Applicants should have (or about to obtain) a first or second class UK honours undergraduate or master degree, or a recognised equivalent from an overseas university in a discipline such as electrical or electronics engineering, physics, computer science or engineering, mathematics, statistics, or other related areas. Good background in computer programming or the desire to develop it is essential

How to apply

If you are interested in applying for the above PhD topic please follow the steps below:

  1. Contact the supervisor by email or phone to discuss your interest and find out if you woold be suitable. Supervisor details can be found on this topic page. The supervisor will guide you in developing the topic-specific research proposal, which will form part of your application.
  2. Click on the 'Apply here' button on this page and you will be taken to the relevant PhD course page, where you can apply using an online application.
  3. Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.

Good luck!

This is a self funded topic

Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. See more information here: https://www.brunel.ac.uk/research/Research-degrees/Research-degree-funding. The UK Government is also offering Doctoral Student Loans for eligible students, and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.

Meet the Supervisor(s)

Liliana Teodorescu - Liliana has joined the Electronic and Computer Engineering Department at Brunel University as a Research Fellow, progressing to a Lecturer and then to the current Senior Lecturer position. She has previously been a Research Fellow at Istituto Nazionale di Fisica Nucleare (INFN), Pisa, Italy, and a Visiting Researcher at Stanford Linear Accelerator Centre (SLAC), USA, and at Thomas Jefferson National Accelerator Facility (Jefferson Lab), USA. Liliana has a diverse experience acquired participating in international large-scale high and medium energy particle and nuclear physics experiments. She combines physics with computer science/engineering and radiation detection instrumentation both in her research and teaching. She has experience in research supervision both at doctoral and post-doctoral level. Liliana served the scientific community by organising high-profile international events at Brunel University such as the 14th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT), and the 33rd CERN School of Computing. She continues her involvement in these events. Liliana is a Fellow of Higher Education Academy (HEA), and a member of the Institution of Engineering and Technology (IET).