Postdoc in Computer Vision and Machine Learning, University of Leeds

Postdoc in Computer Vision and Machine Learning, University of Leeds

Posted by Rebecca Martin on Fri, 04/06/2010 - 00:00

Research Fellow in Computer Vision and Machine Learning
University of Leeds - School of Computing

(Full-time, fixed term position for 14 months)

You will work with Dr Mark Everingham ( on an EPSRC funded project investigating new methods for learning human pose estimation from weak or approximate supervision. The project has three main aims: (i) producing a large dataset of approximately annotated consumer images, at least two orders of magnitude larger than available datasets; (ii) developing machine learning methods to learn from approximate annotation and "side information" for example simple models of human anatomy; (iii) developing strong models of appearance to give robust pose estimation, using the developed machine learning approach. This will include higher order cues modelling appearance of limbs, dependencies between limbs and appearance of joints and configurations of limbs.

You are expected to have a PhD (or to be awarded shortly) in Computer Vision or Machine Learning. You should have experience in developing and applying computer vision and machine learning algorithms, especially probabilistic methods. Expertise in graphical models, structured learning or human pose estimation would be a particular advantage. You should be a proficient programmer in MATLAB and C/C++. You should be self-motivated, good at time management and planning, and have a proven ability to meet deadlines. Good communication and presentation skills are also important.

Salary: Grade 7 (£29,853 - £35,646 p.a)

Apply using: Application form, CV and Equal Opportunities Monitoring form

Application forms:

Informal enquiries: Dr Mark Everingham, tel +44 (0)113 343 5370, email m.everingham(at)

Send completed applications to: Judi Drew, email, or by post to:

Judi Drew
School of Computing
University of Leeds

Closing date: 18 June 2010

Apply online: