Research Fellow in Image Processing Brunel University London, College of Engineering, Design and Physical Sciences United Kingdom

Research Fellow (Image Processing) - HBC0069


College / Directorate
College of Engineering, Design & Physical Sciences
Full Time / Part Time
Full Time
Posted Date
Closing Date
Ref No
  • Job Description
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College: Engineering, Design and Physical Sciences


Department: Brunel Innovation Centre


Institute: Materials and Manufacturing


Theme: Structural Integrity


Job title: Research Fellow – Image processing


Vacancy Ref: HBC0069


Salary (R1): £28,936 to £38,833 per annum plus £2,166 London Weighting per annum


Full-Time, Fixed Term (2 years)


The Brunel Innovation Centre (BIC) is a university research centre located at The Welding Institute’s (TWI) facilities in Cambridge. BIC has been created to carry out research and development in the field of Non-Destructive Testing (NDT) and associated technologies such as sensors and systems for Structural Health Monitoring (SHM) and Condition Monitoring (CM). For more info on BIC, visit


TWI is one of the world's foremost independent research and technology organisations. TWI has a long history of invention and innovation and work across industry sectors including Oil and Gas, Power Generation, Aerospace, Marine and Construction Engineering. TWI are experts in all aspects of materials joining and related technologies, including NDT and guided wave technologies.


BIC has recently been awarded funding by the European Commission to work with companies across Europe in the energy, aerospace and construction & engineering sectors to name a few and is seeking to recruit a Research Assistant / Fellow. 


The objectives of the post is to conduct research in the fields of NDT, SHM and CM. The post will involve work in the following fields:

  • Development of novel digital image processing algorithms for classification, feature extraction, pattern recognition, multi-scale signal analysis, etc.

  • Development of computer vision and pattern recognition algorithms and applications, i.e. object detection and tracking, image/video feature analysis and machine learning.

  • Development and implementation of novel image/video processing and analytics algorithms to detect abnormalities/features which involves:

-        Object profile analysis for missing material detection

-        Pattern recognition for defect detection and classification (type and size of defect)

-        Object tracking from video sequence.

  • Design, coding and deployment of applications for image analytics and support systems.

  • Providing core software engineering practices (writing clear, well documented and testable codes).

  • Developing, debugging and validating codes and software using C/C++, MATLAB, LabVIEW, OpenCV and Python.


The successful candidate will have:

  • A relevant degree or equivalent.

  • Be appropriate for PhD registration or have a PhD in Computer Science/Computer Engineering/Electronics Engineering/IT or related disciplines. 

  • Evidence of sufficient knowledge in the above-mentioned discipline and of research methods.

  • Good mathematical, algorithmic and software development skills. 

  • Strong proficiency in programming.

  • Knowledge and experience in high-performance computing (GPU for 3D modelling/rendering). 

  • Evidence of the ability to acquire and interpret research data and results.

  • Good communication and presentation skills and the ability to draft research papers for publication in academic journals and/or combination of industrial experience and/or training.

  • A high level of initiative and an ability to work both collaboratively and independently. Team-player and willing to work on different projects.

  • Self-driven, results-oriented with a positive outlook and a clear focus on high quality work.

  • A natural forward planner who critically assesses own performance.

  • Excellent analytical capability.


We are supporting the AMSCI funded project AssureNet. This project is a collaboration between SME's, TWI and Academia to deliver a generic quality control method to improve a wide range of advanced net shape manufacturing processes. The aim of the project is to improve supply chains in advanced manufacturing, with supply chains defined as comprising companies who produce components or services that contribute to a finished product.


Closing date for applications: 30/10/2017


For further details and to apply please visit

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