Research Associate or Senior Research Associate in Video Monitoring - ''SPHERE Next Steps'' Project

The role

This research is part of the SPHERE Interdisciplinary Research Collaboration (http://www.irc-sphere.ac.uk/), which has been developing a sensor platform for the home to diagnose and help manage health and wellbeing conditions since October 2013.

The successful candidate will work within the Video Analytics Work Package of the SPHERE NEXT STEPS project, but also collaborate with colleagues in other work packages to develop algorithms and associated models and software to perform computer vision and machine learning tasks.

The successful candidate will work together with several academic staff and the existing RA in a team to investigate algorithms and models for analysing and understanding human behaviour and activity and behaviour, gait and facial and emotion expression in cluttered and uncontrolled home environments. The research and software development will involve feature detection and tracking, deep learning techniques, statistical modelling and analysis, the use of one or more cameras, and many other relevant topics. The post involves close collaboration with other SPHERE project personnel - from other work packages within the project, including integration of other sensors, data fusion and data mining.

What will you be doing

Some of the tasks will be:

• feature detection and tracking
• statistical modelling and analysis
• applying machine learning and deep learning methodologies
• the use of one or more cameras (as well as other sensor modalities), including methods for action recognition and activity analysis
• tracking and identifying across multiple rooms
• and many other relevant topics

You should apply if

• PhD in Video Understanding or Multimodal Computer Vision
• Prior degree in computer science or mathematics
• Detailed knowledge of video understanding state-of-the-art, approaches, datasets and problems
• Experience in handling video data, for learning and inference
• Experience in modelling deep learning approaches for Video Understanding
• Experience and evidence of publishing at high-calibre conferences and journals
• Experience in working within a multidisciplinary or cross-disciplinary team
• Excellent programming skills (Python)
• Proficiency in deep learning frameworks (PyTorch)
• Good publication record in computer vision and/or machine learning·
• Possess excellent speaking and writing skills in English.

We welcome applications from all members of our community and are particularly encouraging those from diverse groups, such as members of the LGBT+ and BAME communities, to join us.