The main aim of this project is to build automated, computer vision and machine learning tools for machine understanding of human behaviour in naturalistic contexts. The envisioned technology will be based on findings in cognitive sciences and it will represent a set of visual spatiotemporal methods for automatic analysis of human spontaneous (as opposed to posed and exaggerated) patterns of behavioural cues such as facial gestures,...
Job listing information
- Reference ENG00940
- Date posted 5 August 2019
- Closing date 20 August 2019
The main aim of this project is to build automated, computer vision and machine learning tools for machine understanding of human behaviour in naturalistic contexts. The envisioned technology will be based on findings in cognitive sciences and it will represent a set of visual spatiotemporal methods for automatic analysis of human spontaneous (as opposed to posed and exaggerated) patterns of behavioural cues such as facial gestures, vocalisations such as laughter and consent, and head gestures.
Duties and responsibilities
Particular attention will be placed on data collection, annotation, curation and organisation for audio-visual human behaviour understanding. This includes the development of new approaches for adapting existing models trained on vast amount of data so they can be applied to audio-visual human behaviour data where small datasets are available.
Within the project, the Research Assistant will be responsible for the development of effective and efficient machine learning algorithms for computer-vision-based understanding of human behaviour that address challenges listed above. The applicant is expected to publish his/her works in top conferences (CVPR, ICCV, ECCV, ICML, and alike) and journal papers (TPAMI, IJCV, TAC, TIP, and other high-impact journals).
The project involves significant research and development challenges, and the end results will contribute to development of more natural human-computer and human-robot interfaces as well as to development of computer-based analysis of human spontaneous (typical or atypical) behaviour displayed in various contexts.
- A good first degree or Masters (or equivalent) in a related area , i.e., Computing, Physics or Engineering.
- A strong background in maths, computing or engineering.
- Proven knowledge and a track record in one or more of the following areas: machine learning, computer vision and statistical modelling
- Advanced programming skills in deep learning frameworks (PyTorch, Tensorflow)
- Advanced programming skills in Python
- Experience of processing audio/visual data
- Experience of deep learning and machine learning
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- A 1-page statement indicating why your expertise is relevant.
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