Research Associate in Computer Vision
University of Sheffield - Department of Automatic Control and Systems Engineering
|Salary:||£30,175 to £32,004 per annum, Grade 7|
|Contract Type:||Contract / Temporary|
|Placed on:||13th October 2016|
|Closes:||24th October 2016|
|★ View Employer Profile|
Contract Type: Fixed term from 1 December 2016 to 28 February 2017
Faculty: Faculty of Engineering
Automatic Control and Systems Engineering (ACSE) is one of the largest departments devoted to the subject in Europe, with 24 academic staff, 28 research staff, 20 professional and support staff and nearly 400 taught and research students. We are an internationally leading research department with a vibrant culture spanning all aspects of Systems and Control Engineering. Our research addresses many of the complex issues of modern life including problems in the life-sciences and healthcare, aerospace and transport, energy and the environment, manufacturing and robotics and, more recently, social policy. Our approach to these challenges focuses on generic theoretical developments in complex systems with an overarching emphasis on autonomy and decision-making. For more information on the Department please see our web site http://www.shef.ac.uk/acse/.
The Research Associate will contribute to an Innovate UK project aimed at improving decision making on autonomous cars by making use of human driver experience, Funded by the TSB (Innovate UK), by the University of Sheffield and by Floow Ltd, on “Driver experienced Based Learning of Decisions by Autonomous Cars”, the project will focus on the delivery of computer vision and data fusion methods for statistical learning of driving skills.
Candidates will have a PhD or equivalent experience in computer science or engineering, and programming experience in OpenCV / C++/ROS/GPUs. Experience as a software developer using object-oriented design methods is also essential for the role. Effective communication skills are essential, as is the ability to analyse problems with an appreciation of the longer-term implications.
Specific benefits linked to this post include being part of a leading research team addressing state-of-the-art industrial design optimisation problems working closely with Floow Ltd.
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