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RA in Fine grained Video Understanding

University of Bristol - Engineering - School of Computer Science, Electrical and Electronic Engineering and Engineering Maths

Location: Bristol
Salary: £34,304 to £38,587 per annum
Hours: Full Time
Contract Type: Permanent
Placed On: 25th August 2021
Closes: 30th September 2021
Job Ref: ACAD105536

The role

A strong and vibrant research team with steady-stream publications in high-calibre venues is looking for a postdoctoral researcher (36-months fixed-term) to develop novel approaches to fine-grained video understanding. The position is part of an EPSRC Fellowship grant for PI Dima Damen. Quoting from the fellowship’s aim: “To achieve human-level perception of object interactions, including online perception when the interaction results in mistakes (e.g. water is spilled) or risks (e.g. boiling water is spilled), this fellowship focuses on informing computer vision and machine learning models, including deep learning architectures, from well-studied cognitive behavioural frameworks.”

You will be working closely with Dima on her active research. Check Dima’s research interests and projects at:

Prior expertise in video analytics and deep learning methods with a strong publication track record is expected, including first-author publications in CVPR/ICCV/ECCV/BMVC/PAMI/IJCV/NeurIPS/ICLR.

What will you be doing?

Over the next 16 months, you will be:

  • Conducting novel research in video understanding – contributing novel research on modularisation of end-to-end approaches for fine-grained understanding. This will include hands-on research using the latest deep learning approaches.
  • Presenting your work in regular meetings, taking feedback and integrating the goals of the program grant into your individual research directions.
  • Publishing in top-tier venues (conferences and journals). Communicating your work to the best possible audience.
  • Co-advising junior PGR students

You should apply if

  • PhD in Video Understanding, preferably with expertise in egocentric (first-person) video understanding.
  • Prior degree in computer science, engineering or mathematics
  • Detailed knowledge of video understanding state-of-the-art, approaches, datasets and problems, preferably with expertise in egocentric datasets and familiarity with EPIC-KITCHENS dataset.
  • 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 (at least one first-author paper in a major venue – CVPR/ICCV/ECCV/BMVC/NeurIPs/PAMI/IJCV/Neurips/ICLR in the past 3 years).
  • Excellent programming skills (Python)
  • Proficiency in deep learning frameworks (PyTorch)

Additional information

For enquiries, Dima Damen:

To find out more about what it's like to work in the Faculty of Engineering, and how the Faculty supports people to achieve their potential, please see our staff blog:

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.

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