Research Fellow in Computer Vision and Machine Learning

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

(Full Time, Fixed term 14 months)

You will work with Dr Mark Everingham ( on an
EPSRC funded project investigating new methods for learning articulated human
pose estimation from weak or approximate supervision. The project has three
main aims: (i) developing machine learning methods to learn from approximate
annotation and “side information” for example simple models of human anatomy;
(ii) 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; (iii) producing a large dataset of
approximately annotated consumer images, at least two orders of magnitude
larger than available datasets. Further information about the project can be
found online:

Applicants are expected to have a PhD (or to be awarded shortly) in a related
topic. 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)

Job description and person specification:

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

Download an application form:

Informal enquiries:

Dr Mark Everingham, email: m.everingham(at)

Send completed applications to:
Judi Drew, email j.a.drew(at) or by post to:

Judi Drew
School of Computing
University of Leeds

Closing date: 22 September 2010 at 17:00 GMT

Anticipated interview date: 01 October 2010