Research Associate in Computer Vision & Machine Learning for Tracking and Scene Segmentation
An opportunity has arisen within the Department of Electrical & Electronic Engineering at Imperial College London for a Research Associate to work on Computer Vision & Machine Learning for People and Vehicle Tracking and Scene Segmentation. The successful applicant will become a member of the Personal Robotics Laboratory (led by Professor Yiannis Demiris, RAEng Chair in Emerging Technologies), a team of 20 postdoctoral, PhD and MSc...
Job listing information
- Reference ENG01056
- Date posted 15 October 2019
- Closing date 13 November 2019
An opportunity has arisen within the Department of Electrical & Electronic Engineering at Imperial College London for a Research Associate to work on Computer Vision & Machine Learning for People and Vehicle Tracking and Scene Segmentation.
The successful applicant will become a member of the Personal Robotics Laboratory (led by Professor Yiannis Demiris, RAEng Chair in Emerging Technologies), a team of 20 postdoctoral, PhD and MSc researchers, with significant expertise in human-robot interaction, human-centred computer vision, assistive robotics, vehicle intelligence, machine learning and user modelling.
This is a fixed term appointment up to 31st of October 2021 in the first instance. The start date of the project is on the 1st of Nov 2019 (with some degree of flexibility) and the salary will be based on the level of relevant experience of the successful candidate.
The successful applicant will also be working closely with researchers at the Transport Systems & Logistics Laboratory (Department of Civil & Environmental Engineering), the Institute of Security Science and Technology, our industrial partners (Drisk.ai, Claytex, Digital Greenwich) and Transport for London.
Duties and responsibilities
The postholder will contribute to the development of Tracking and Scene Segmentation algorithms using Computer Vision and Machine Learning techniques. These will be used to automatically extract vehicle, cycle, and pedestrian behaviors from traffic video feeds and other spatiotemporal data sources. The extracted data structures will be used for the indentification of dangerous scenarios and anomaly detection, as well as training data for learning algorithms to perform and verify the safety of navigation control algorithms for autonomous vehicles and robots.
For appointment at Research Associate level you will hold a PhD (or equivalent) in Computer Science, Electrical Engineering or Mathematics (or equivalent). You will have:
• Previous experience in one or more of: computer vision, tracking, scene segmentation, deep learning, machine learning. Preference will be given to people with experience in the processing of dynamic scenes (spatiotemporal data).
• Practical experience within a research environment and/or publication(s) in relevant high quality refereed journals and conferences.
• Strong software engineering skills, e.g. in C++/Python, with a demonstrable record of experience in implementing substantial algorithms.
• Strong interest in designing, implementing and evaluating computer vision systems for the automated analysis of video feeds for the extraction of human and vehicle behaviours.
*Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant within the salary range £35,477 - £38,566 per annum.
For informal enquiries about the post please contact Professor Yiannis Demiris at email@example.com.
Our preferred method of application is online via our website by clicking ‘apply’ below or go to https://www.imperial.ac.uk/job-applicants/ and search using reference number ENG01056.
Queries regarding the application process should go to Joan O’Brien at firstname.lastname@example.org.
Further information about the post is available in the job description.
About Imperial College London
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Please note that job descriptions cannot be exhaustive and the post-holder may be required to undertake other duties, which are broadly in line with the above key responsibilities.
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