Research Fellow in Machine Learning and Visual Data Queen’s University Belfast United Kingdom

Queen’s University Belfast
Research Fellow - Machine Learning & Visual Data
School of Electronics, Electrical Engineering & Computer Science (EEECS)
Ref: 19/107686

Queen’s University Belfast is one of the leading universities in the UK and Ireland, with a distinguished heritage and history. With over 24,000 students, 4,200 staff and an annual turnover of some £300m, Queen’s University Belfast plays a unique leadership role in Northern Ireland. As a member of the Russell Group of UK research-intensive universities, Queen’s University Belfast combines excellence in research and education with a student centred ethos.

This is a fixed term contract until 30 May 2021. This research project aims at utilizing existing large-scale RGB domain data to reduce the requirements of IR-domain data (or the other domain data) for general object classification and detection by means of deep domain adaptation technique.

This position provides a unique opportunity to address the problem of domain adaptation and apply to real-world scenarios. The project is hosted by the Centre for Data Science and Scalable Computing (DSSC) in the Institute of Electronics, Communications and Information Technology (ECIT), at Queen’s University Belfast, UK and collaborating with Defence Science and Technology Agency (DSTA), Singapore.

With the enormous amount of advancement made by the deeper and broader blending of deep learning methods into computer vision applications, the need of large-scale labelled dataset becomes a significant obstacle every time when a new task is raised. Domain adaptation is a branch in transfer learning where a model that is trained in a source domain is adapted to another target domain. Usually, the source domain is with labelled data while no or limited labelled data are available in the target domain.

School of Electronics, Electrical Engineering and Computer Science perform world class research across within the Queen’s Global Research Institute on Electronics, Communications and Information Technology (ECIT), the Pioneer Research Program on Intelligent Advanced Manufacturing Systems (iAMS), and a core disciplinary research Cluster in Electronics and Computer Engineering (ECE). Our Electrical and Electronic Engineering (EEE) research was ranked 5th in the UK in the 2014 Research Excellence Framework.

The successful candidate must have:

• A good undergraduate degree in a relevant discipline and a PhD in computer vision, machine learning, data science, or mathematics or a clearly related area.
• At least 3 years relevant research experience.
• A proven track record of research ability in computer vision and machine learning
• Experience in delivering research project results in computer vision and machine learning, i.e. a record of peer-reviewed journal and conference papers in a relevant area.

Our five core values (Integrity, Connected, Ambition, Respect, Excellence) have been developed by staff and students. They express our shared understanding of what we believe, how we aim to behave and what we aspire to be as an international organisation.

Further information about the School can be found at http://www.qub.ac.uk/schools/eeecs/

Anticipated interview date: Friday 30 August 2019

Salary scale: £33,199 to £35,210 per annum

Closing date: Wednesday 14 August 2019

For full job details and essential/desirable criteria please refer to the candidate information link on our website by clicking apply. You must clearly demonstrate how you meet the criteria when you submit your application. For further information or assistance contact Resourcing Team, Queen’s University Belfast, BT7 1NN. Telephone (028) 9097 3044 or email on resourcing@qub.ac.uk

The University is committed to equality of opportunity and to selection on merit. It therefore welcomes applications from all sections of society and particularly welcomes applications from people with a disability

Fixed term contract posts are available for the stated period in the first instance but in particular circumstances may be renewed or made permanent subject to availability of funding.


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