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Research Fellow in Computer Vision/Machine Learning Engineer
Ref. 50051326

Salary: REB - £40,173 pro-rata per annum (Inc. LWA)

Department: School of Computer Science and Engineering

Location: Central London

Part-time 28 hours per week (0.8 FTE) and fixed term for 12 months from September 2018.

We are seeking to appoint a Research Fellow to develop innovative image/video analysis and machine learning tools to support early screening of dementia in the ageing British Sign Language (BSL) population. These tools will assist clinicians who have limited knowledge about BSL to understand if these changes in signing are associated with acquired neurological impairments or with changes associated with normal ageing. The tools will be developed and tested on two datasets:
  1. Video recordings of 60 signers aged over 50 from 8 different regions of the UK in conversation and responding to interview questions; 
  2. Video recordings as well as cognitive and language test scores on the BSL Cognitive Screen of 250 signers aged between 50 and 80, including signers with mild cognitive impairment and early stage dementia.

This post is fully funded by the Dunhill Medical Trust via the Research Project Grants (http://dunhillmedical.org.uk/). This project is in collaboration with the Deafness Cognition and Language Research Centre (DCAL) at UCL.

You will work closely with Dr Anastassia Angelopoulou and Dr Epaminondas Kapetanios (Computer Science, UoW) and Professor Bencie Woll and Dr Joanna Atkinson (linguistics of BSL and cognition, UCL).

You will be expected to have an excellent background in machine learning and especially deep neural networks, and computer vision methods such as predicting depth estimation from single camera recordings. Advanced level programming skills using a variety of languages and platforms are expected. Deaf Awareness Training and instruction in BSL will be provided.

The research Fellow’s responsibilities include developing medical software for quantitative assessment of early screening of Dementia in the ageing British Sign Language (BSL) population. The successful candidate must be able to frame a problem statement from mathematical, physical and application perspective, and perform model training / algorithm development and optimization. In addition to this, also Design, develop and deploy solutions using computer vision and machine learning approaches, test software and documentation of test results. The successful candidate will also need to collaborate in a team environment with both deaf and hearing colleagues across multiple research disciplines and present project updates at conferences, workshops and meetings. 

You will be part of a well-balanced, multi-disciplinary team, which includes Deaf and hearing experts from the areas of computer vision, machine learning, pattern and semantic search, and the linguistics of BSL, as well as cognition and neuroscience in Deaf people. You will be a member of the Cognitive Computing research group (C2) based at the University of Westminster’s New Cavendish campus and the Deafness Cognition and Language Research Centre (DCAL) at UCL. You are expected to spend time at both Institutions.

To apply for this vacancy, please click above. Further information is included in the job description and person specification, which can be accessed through link below. 

Closing date: midnight on 31 July 2018

Interviews are likely to be held on: 23 August 2018

Administrative contact (for queries only): Recruitment@westminster.ac.uk 

For an informal discussion about this post, please contact Dr Anastassia Angelopoulou, Principal Investigator, by e-mail in the first instance: agelopa@westminster.ac.uk

Please note: We are unable to accept applications by email. All applications must be made online. CV’s in isolation or incomplete application forms will not be accepted. 

We are fortunate to receive a large number of applications for our vacancies. Regrettably, we are not able to provide feedback to those job applicants who are not shortlisted, as it simply would not be manageable to do so. 

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