3D human pose estimation and shape reconstruction for biomechanics
Applications are invited for Fully Funded PhD Studentship at the University of Nottingham, United Kingdom.
Details
The Fully Funded PhD Studentship research topic focuses on developing computer vision and machine learning based solutions that enable in-natura markerless motion capture for biomechanical modelling in Biomedical and Sports Engineering.
Specifically, it addresses the fundamental research problem of reconstruction of person-specific human pose, kinematics, and surface geometry to enhance our understanding of the non-linear behaviour of human motion, musculoskeletal injury and disease and enable modelling of soft-tissue dynamics and human-object interaction.
The project
The applicant is expected to develop a fast and robust method for inferring and tracking 3D human pose and surface geometry. The method will be mainly based on visual sensing complemented by Inertial and force sensors. The method can use either or both of model-based and learning-based approaches, such as CNN based segmentation, geometric CNNs, or convolutional kernel filter based tracking. The applicant will have access to a newly established state-of-the-art motion capture laboratory.
Benefits
Successful applicants will receive the following benefits:
- a tax-free stipend of 15,285 per year (for 2020/21)
- tuition fee
Eligibility
To be eligible for Fully Funded PhD Studentship, applicants must have:
- a first or upper second class honours or Masters degree in Electrical and Electronic Engineering, Physics, Computer Science, or other relevant and equivalent degree from a quality recognised institution
- a solid background in mathematics and excellent analytical and numerical skills, as well as problem solving skills
- strong background in 3D computer vision, pose estimation, shape reconstruction, structure from motion, segmentation, or object detection
- experience in image or video processing and digital signal processing
- strong programming skills in Matlab, C/C++, or Python. Previous hands-on experience with deep learning platforms and agile software development as well as experience of working within industry will be an advantage
- very good written and communication skills and fluency in English
- a driven, independent professional and self-reliant work attitude within a fast-paced & collaborative environment
Application
For Fully Funded PhD Studentship, applicants are requited to submit:
- Cover Letter
- Curriculum Viate
- Copies of academic transcripts
- A list of publications
- Contact details for two academic referees
e-mail a single pdf file to: Ami.Drory@nottingham.ac.uk, with [3D shape reconstruction PhD application – lastName] as the email subject.
