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Research Associate in Computer Vision and Machine Learning

taekyun999 – Posted on November 8, 2017 at 5:27 am –


IMPERIAL COLLEGE LONDON

Department of
Electrical and Electronic Engineering

Research Associate* in Computer Vision and
Machine Learning

Fixed Term  for up to 24 months

Immediate
start available

Imperial College London is a
science-based institution with the greatest concentration of high-impact
research of any major UK university.  The
Faculty of Engineering, consistently rated among the best in the world, is made
up of 10 academic departments and is committed to increasing its research
activity by focusing on engineering-led multidisciplinary growth areas that
target a number of global challenges. All of our academic departments are
located on a single campus in South Kensington, giving a concentration of
talent that creates a stimulating and vibrant research community. You can find
out more about our staff benefits, including generous annual leave entitlements
and our excellent professional development opportunities, here: http://www.imperial.ac.uk/job-applicants/staff-benefits/.

Applications are invited for a
Research Associate position within the computer vision and learning lab (http://www.iis.ee.ic.ac.uk/ComputerVision)
that belongs to the Intelligent Systems and Networks Research Group (
http://www3.imperial.ac.uk/intellisysnetworks), Department of Electrical and Electronic Engineering, Imperial College
London. The position is available under the supervision of Dr Tae-Kyun Kim.

The project focuses
on computer vision and machine learning, articulated pose estimation and
detection/tracking 
Applicants
will hold a PhD (or equivalent)
in computer vision or machine learning with a strong
track record in the relevant field (
CVPR/ICCV/ECCV and PAMI/IJCV). Experience in the
aforementioned topics, real-time vision algorithms, deep learning and
randomised decision forests, or any such relevant expertise is ideal.

The post will involve adhering to deadlines for
project deliverables, so programming and demonstrative skills along with the
ability to deliver to specified deadlines is required.

Enquires may be sent to Dr Tae-Kyun
Kim (tk.kim@imperial.ac.uk).

Closing
Date:  as soon as the post is filled  


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