Research Assistant in User-assured Object Detection

£31,342-£35,256 fixed term for up to 12 months

Ref: ACAD101142

Based in a research team at the Visual Information Laboratory (VIL) within the Merchant Venturer’s School of Engineering at the University of Bristol this is an exciting opportunity developing state of the art techniques for assured object detection and classification.

Building on your strong knowledge in machine learning and computer vision, you will develop, test and evaluate new methods for assured object detection and classification in sonar imagery, working in close collaboration with both our partners at DSTL and Cambridge University who will provide essential information for model generation and validation.

You will work on the development of custom written codes to combine object detection and classification techniques with contextual information such as region or terrain type classifications in order to produce user-relevant reliability information associated with detection results. Your tasks will also include the reporting and dissemination of results and communication with our partners.

You will have a PhD in Computer Vision, Machine Learning or a closely related field in Computer Science or Engineering and have good experience of visual classification or detection techniques and programming as well as a wider knowledge of computer vision.

This post is offered on a full time fixed term contract for up to 12 months with an immediate start date.

Informal enquiries can be made to: Dr Tilo Burghardt burghard@cs.bris.ac.uk or Prof David Bull Dave.Bull@bristol.ac.uk

Appointment may be either on a fixed term or a permanent contract depending on the extent of previous relevant research experience, in line with the University’s Fixed Term Contract Agreement. Further information can be found at www.bristol.ac.uk/hr/ftc/

The University of Bristol is committed to equality and we value the diversity of our staff and students.

To apply, please click here to be redirected to our website, enter the vacancy number into the job search and follow the link to the on line application process.

The closing date for applications is 16 December 2014.

It is anticipated that interviews will take place in December 2014