QUEENS UNIVERSITY BELFAST

Research Fellow, Machine Learning and Visual Data

Location
Belfast, Northern Ireland
Salary
£33,199 to £35,210 per annum
Posted
15 Jul 2019
End of advertisement period
14 Aug 2019
Ref
19/107686
Contract Type
Fixed Term
Hours
Full Time

Application closing date 14/08/2019
Salary £33,199 to £35,210 per annum
Job category/type Research

Job description

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 EEECS 

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.

Candidate Information
About the School
Further information for international applicants
Note to EEA applicants on Brexit