Deep Learning-Based Defect Detection for Metal Additive Manufacturing

We are recruiting new Doctoral Researchers to our EPSRC funded Doctoral Training Partnership (DTP) PhD studentships starting 1 October 2023. Applications are invited for the project Deep Learning-Based Defect Detection for Metal Additive Manufacturing 

Successful applicants will receive an annual stipend (bursary) of approximately £19,668, including inner London weighting, plus payment of their full-time home tuition fees for a period of 42 months (3.5 years).

You should be eligible for home (UK) tuition fees there are a very limited number (no more than three) of studentships available to overseas applicants, including EU nationals, who meet the academic entry criteria including English Language proficiency.

You will join the internationally recognised researchers in the Department of Computer Science

The Project

This exciting research project is focused on the defect detection of metal additive manufacturing (MAM) components, which aims to develop a novel defect detection framework capable of evaluating the quality of an AM component via a data-driven pipeline. The focus of this project is: 1) developing deep learning algorithms for inline non-destructive testing; and 2) building a knowledge-based system on intelligent data analysis of various sensor data that could assist in the manufacturing of new components.  

Please contact Dr Weibo Liu at weibo.liu2@brunel.ac.uk for an informal discussion about the studentships.

Eligibility

Applicants will have or be expected to receive a first or upper-second class honours degree in an Engineering, Computer Science or Mathematics. A Postgraduate Masters degree is not required but may be an advantage.

Skills and Experience

Applicants will be required to demonstrate their experience in data mining, big data, machine learning, statistics, outlier detection, time-series analysis, image processing, multi-sensor fusion, many-object optimization, evolutionary computation, GPU programming, cloud computing, and additive manufacturing.

Candidates should be highly motivated, able to work independently as well as in a team, collaborate with others and have good communication skills.

How to apply

There are two stages of the application:

1.Applicants must submit the pre-application form via the following link https://brunel.onlinesurveys.ac.uk/epsrc-dtp-23-24-pre-application-form-brunel-university-lon-3 by 16.00 on Friday 26th May 2023.

2.If you are shortlisted for the interview, you will be asked to email the following documentation in a single PDF file to cedps-studentships@brunel.ac.uk within 24hrs.

Your up-to-date CV;
Your Undergraduate degree certificate(s) and transcript(s) essential;
Your Postgraduate Masters degree certificate(s) and transcript(s) if applicable;
Your valid English Language qualification of IELTS 6.5 overall (minimum 6.0 in each section) or equivalent, if applicable;
Contact details for TWO referees, one of which can be an academic member of staff in the College.

Applicants should therefor ensure that they have all of this information in case they are shortlisted.

Interviews will take place in June 2023.

Meet the Supervisor(s)

Weibo Liu - Dr. Weibo Liu received the B.S. degree in electrical engineering from the Department of Electrical Engineering & Electronics, University of Liverpool, Liverpool, UK, in 2015, and the Ph.D. degree in artificial intelligence in 2020 from the Department of Computer Science, Brunel University London, Uxbridge, UK. He is currently Lecturer in the Department of Computer Science, Brunel University London, U.K. His research interests include big data analysis, evolutionary computation, machine learning and deep learning techniques. He serves as an Associate Editor for the Journal of Ambient Intelligence and Humanized Computing and the Journal of Cognitive Computation. He is a very active reviewer for many international journals and conferences.

Related Research Group(s)

Robotics and Automation - We carry out world-class research in robotics and autonomous systems, exploiting and exploring opportunities to develop innovative solutions for industrial and societal applications.

Additive Manufacturing and 4D Printing - The Additive Manufacturing and 4D Printing Research Group seeks to understand the fundamental aspects and applications of programmable materials through layer-wise methods of production.

Intelligent Data Analysis - Concerned with effective analysis of data involving artificial intelligence, dynamic systems, image and signal processing, optimisation, pattern recognition, statistics and visualisation.