Research Associate in Computer Vision & Machine Learning for Tracking and Scene Segmentation

Job summary

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) a team of 20 postdoctoral, PhD and MSc researchers, with significant expertise in...

Job listing information

  • Reference ENG00470
  • Date posted 7 August 2018
  • Closing date 10 September 2018

Job description

Job summary

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) 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 30th of September 2020 in the first instance. The start date is on the 1st of October 2018 (with some degree of flexibility) and the salary will be based on the level of relevant experience of the successful candidate.

The post is funded from Innovate UK and the Centre for Connected and Autonomous Vehicles and linked to an exciting collaborative project that will develop a simulation-based safety validation framework that will model the interaction between autonomous vehicles (SAE Level 4/5) and other road users with an emphasis on the automated identification of safety edge-cases.

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 (aiPod, 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.

Furthermore, they will be contributing to ongoing research in the area of Autonomous Vehicle Operations spanning both the Electrical & Electronic Engineering and Civil & Environmental Departments, encompassing the aspects of mobility service design using autonomous vehicles (taxi-bots), systems development and impact assessment.

Essential requirements

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.
• Practical experience within a research environment and/or publication(s) in relevant 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 £33,380 - £35,061 per annum.

Further information

For informal enquiries about the post please contact Professor Yiannis Demiris at y.demiris@imperial.ac.uk.

Our preferred method of application is online via our website. Please click ‘apply’ below or go to https://www.imperial.ac.uk/job-applicants/ and search using reference number ENG00470.

Any queries regarding the application process should be directed to Ms Joan O’Brien at j.obrien@imperial.ac.uk.

Further information about the post is available in the job description.

Closing Date: 10 September 2018

Interviews will take place in the week following the closing date of this advert.

Documents

About Imperial College London

Imperial College London is the UK’s only university focussed entirely on science, engineering, medicine and business and we are consistently rated in the top 10 universities in the world.

You will find our main London campus in South Kensington, with our hospital campuses located nearby in West and North London. We also have Silwood Park in Berkshire and state-of-the-art facilities in development at our major new campus in White City.

We work in a multidisciplinary and diverse community for education, research, translation and commercialisation, harnessing science and innovation to tackle the big global challenges our complex world faces.

It’s our mission to achieve enduring excellence in all that we do for the benefit of society – and we are looking for the most talented people to help us get there.

Additional information

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.

All Imperial employees are expected to follow the 7 principles of Imperial Expectations: 

  • Champion a positive approach to change and opportunity
  • Communicate regularly and effectively within, and across, teams
  • Consider the thoughts and expectations of others
  • Deliver positive outcomes
  • Encourage inclusive participation and eliminate discrimination
  • Develop and grow skills and expertise
  • Work in a planned and managed way 

In addition to the above, employees are required to observe and comply with all College policies and regulations.

Imperial College is committed to equality of opportunity, to eliminating discrimination and to creating an inclusive working environment. We are an Athena SWAN Silver award winner, a Stonewall Diversity Champion, a Disability Confident Employer and work in partnership with GIRES to promote respect for trans people.