Job description
The role
The role requires the appointee to study and analyse state-of-the-art approaches in human pose understanding, and be able to propose methods, frameworks and novel solutions for the objectives of the TORUS project to monitor and quantify the actions of Parkinson’s patients in home environments.
What will you be doing?
Assist or lead in developing algorithms and software, independently and collaboratively, with other members of the TORUS project. Similarly, to identify and/or use tools and libraries (e.g. AI-enabled cameras, GPUs and GPU servers, and other required aspects of the computer vision related works in TORUS). Attend meetings and report regularly to line managers and in other TORUS-related events. Write and produce high quality papers for submission to top-tier conferences and journals. Also see other duties as referred to in the associated Job Description document.
You should apply if
- PhD in Video Understanding or Multimodal Computer Vision
- 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 in edge computing and deep learning
- Experience in AI-enabled camera systems
- Publications as 1st author in top-tier computer vision and machine learning conferences and journals
- Excellent programming skills (Python)
- Proficiency in deep learning frameworks (PyTorch and/or Tensorflow)
Additional information
Contract type: Open ended with funding until 30/09/2026
Work pattern: Full-time
Grade/Salary: I £36,333 - £40,745 per annum / J £40,745 - £45,737 per annum
School/Unit: Department of Computer Science, Faculty of Engineering
For informal queries, please contact Prof. Majid Mirmehdi - Email: [email protected] or Dr Alessandro Masullo – Email: [email protected]
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:
https://engineeringincludesme.blogs.bristol.ac.uk/
Interviews are anticipated to take place on or around 21st or 22nd of August 2023.
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Available documents
- ACAD106954 - Job Description.pdf
JOB NUMBER
ACAD106954
CONTRACT TYPE/WORK PATTERN
Open ended / Full time
CLOSING DATE
13 Aug 2023
FACULTY/DIVISION
Faculty of Engineering
SALARY