Post-doc : improving existing deep learning models to estimate physiological signals from the video signal
The ImViA lab at University Burgundy (Dijon – France), together with Honda Research Institute (Japan), invites applications for postdoctoral research positions in deep learning for video analysis.
The postdoctoral researcher will work on improving existing deep learning models to estimate physiological signals from the video signal. Remote photoplethysmography (rPPG) is a recent technique for estimating heart rate and other vital signs by analyzing subtle skin color variations using regular cameras (see  for an interesting review). More recently, end-to-end approaches based on deep learning have also been used. We will seek to extend existing work by improving current models, focusing on night vision applications. The candidate will take part in ongoing projects and possibly initiate new research within the team.
The postdoctoral researcher will work in Dijon – France in collaboration with researchers from the Honda Research Institute in Japan. This fellowship has a duration of 12 months with possibility of extension.
As part of this postdoc, we can offer generous support for professional travel and research needs.
We are seeking a highly qualified and motivated candidate with a Ph.D. in Computer Vision, Machine Learning, Image processing, Biomedical Engineering, or a closely related field with a relevant scientific track record on significant computer vision conferences/journals as well as experience on deep learning techniques and frameworks.
Interested candidates should submit their CV, letter(s) of reference, and a brief research statement describing their background and research interests and how they align with the project emailed to Yannick Benezeth (email@example.com). The call will remain open until the position is filled. The postdoc contract will start as soon as possible.
 Rouast, P. V., Adam, M. T., Chiong, R., Cornforth, D., & Lux, E. (2018). Remote heart rate measurement using low-cost RGB face video: a technical literature review. Frontiers of Computer Science, 12(5), 858-872.