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Research Fellow - Deep Learning and Computer Vision

Vision, Learning and Control

Location:  Highfield Campus
Salary:   £30,395 to £32,236 per annum
Full Time Fixed Term - until 31/05/2020
Closing Date:  Sunday 09 December 2018
Interview Date:   Thursday 13 December 2018
Reference:  1083818FP

The Vision, Learning and Control (VLC) group (www.vlc.ecs.soton.ac.uk ) within the School of Electronics and Computer Science at the University of Southampton is looking to recruit a Research Fellow for an exciting joint project called 'Transcribe AI', which is looking at automated information extraction from document images. The project is funded by Innovate UK and is in collaboration with Evolution AI (http://evolution.ai ). The successful candidate will be positioned within the vibrant community of the VLC research group, but will have the opportunity to work closely with engineers at evolution.ai.

Candidates for this position will have completed a PhD* or equivalent professional qualifications and experience in an area related to deep learning and/or computer vision. The candidate will have a detailed understanding and knowledge of standard approaches in deep learning and machine learning, including recurrent and convolutional neural networks. Experience in using and implementing models in standard libraries such as pytorch or tensorflow is essential. 

The following criteria are desirable for this role: 

  • Knowledge of recent deep learning techniques, including attentional models, residual networks and recurrent-convolutional architectures; 

  • Knowledge of recent approaches in computer vision; 

  • Experience of working large volumes of visual data; 

  • Experience of the use of distributed and multi-GPU deep learning platforms.

The application must show clear evidence of their relevant skills in all these fields.

The post is available until 31/05/2020 in the first instance on a full-time basis.

Informal enquiries should be made to Dr Jonathon Hare, jsh2@ecs.soton.ac.uk.

* Applications for Research Fellow positions will be considered from candidates who are working towards or nearing completion of a relevant PhD qualification. The title of Research Fellow will be applied upon successful completion of the PhD. Prior to the qualification being awarded the title of Senior Research Assistant will be given.

About the research environment

The post is based in the Vision, Learning and Control (VLC) Research Group within the School of Electronics and Computer Science (ECS). VLC is home to a large and diverse group of researchers investigating various aspects of machine learning and computer vision. The group holds a number of regular events such as reading and discussion groups which the research fellow will be able to engage with. 

ECS is the UK’s leading academic unit integrating computer science, electronics, and electrical engineering. In the 2014 Research Excellence Framework, ECS was first in the UK for the volume and quality of research in Electrical and Electronic Engineering.  The volume and quality of ECS Computer Science research is in the top 10 in the UK, with 100% of Computer Science research impact rated as world-leading or internationally excellent.

Due consideration will be given to applicants who have taken a career break. The University of Southampton holds an Athena Swan Silver Award.

The University has a generous maternity policy and onsite childcare facilities; employees are able to participate in the childcare vouchers scheme. Other benefits include state-of-the-art on-campus sports, arts and culture facilities, a full programme of events and a range of staff discounts.

Application Procedure:

You should submit your completed online application form at https://jobs.soton.ac.uk. The application deadline will be midnight on the closing date stated above. If you need any assistance, please call Rosie McPhail (Recruitment Team) on +44 (0) 23 8059 4043. Please quote reference 1083818FP on all correspondence. References are requested along with your application. This reference system is automated so please ensure you provide the correct email address and allow time for these to be received, prior to the close date, to assist the department with shortlisting.

The closing date for this job opportunity has now passed, and applications are no longer being accepted for this position

Further details:

We aim to be an equal opportunities employer and welcome applications from all sections of the community. Please note that applications from agencies will not be accepted unless indicated in the job advert.


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