Research Fellow in Person Re-Identification and Tracking
Department of Electrical & Electronic Engineering
Salary: £31,302 to £39,609 per annum
Post Type: Full Time
Closing Date: Sunday 21 July 2019
Applications are invited from enthusiastic and talented individuals for a post–doctoral research position as part of an interdisciplinary research project. The position is available from 1 August 2018 through an EPSRC funded project in the area of deep learning for automatic face recognition – FACER2VM. The work will be undertaken at the University of Surrey (UK) under the supervision of Professor Josef Kittler.
FACER2VM is a multidisciplinary collaborative research project funded by EPSRC. This project, led by the University of Surrey, involves Imperial College London and the University of Stirling, and is supported by the Home Office Centre for Applied Science and Technology, IBM, Digital Barriers, Cognitec, 3rdForensic, and the European Association for Biometrics. The aim of the project is to develop solutions for unconstrained face recognition in which the engineering research will be informed by the psychology of human face matching. In particular, FACER2VM will focus on image degradations caused by lighting, resolution, occlusion and noise, as well as coping with natural modes of variations such as head pose and expression. The project offers an opportunity for successful candidates to work in an inspiring environment at the University's Centre for Vision, Speech and Signal Processing, one of the UK's premier research centres in image processing.
As part of this project, we offer a full-time postdoctoral research position (RA1A) for two years. Although you will be based at the University of Surrey, you will be expected to travel occasionally to Imperial College London and University of Stirling for meetings and joint activities with our collaborators. In particular, your task will be to work on various aspects of unconstrained face recognition using machine learning, andto integrate the algorithms developed into a face recognition system, as well as evaluating their effectiveness on data provided by the project partners.
We are looking for a researcher with a PhD degree or currently enrolled on a PhD programme, with good skills and knowledge in face analysis and matching. Knowledge of person re-identification and tracking, as well as face recognition and modelling will be highly advantageous, as is experience in machine learning, deep neural networks and in handling large datasets. You need to have good programming and experimental skills and be confident working at the interface of image/video processing and machine learning. You should have experience of C/C++ and GPU programming.
The candidates should be motivated and enthusiastic to work in a team environment and should have experience of communicating findings in journal papers. We are also looking for good written and spoken English language skills.
You will be expected to conduct research in Streams 1, 4 and 6 of the FACER2VM project concerned with person identification and re-identification, with a particular responsibility for people and face tracking in continuous video data stream. The duties will include reporting the results in top ranking pattern analysis and biometrics journals. You will actively contribute to FACER2VM project system integration activities and evaluation campaigns, as well as to outreach and technology transfer activities.
For informal enquiries please contact Professor Josef Kittler (J.Kittler@surrey.ac.uk), tel. +44 (0)1483 689294. If you are unable to apply online, please contact Bianca Barrett B.Barrett@surrey.ac.uk tel: +44 (0) 1483 683419.
Please note, it is University Policy to offer a starting salary equivalent to Level 3.6 (£31,302) per annum (full-time) to successful applicants who have been awarded, but are yet to receive, their PhD certificate. Once the verified original PhD certificate has been submitted to the local HR Department, the salary will be increased to Level 4.1 (£32,236) per annum (full time).
For more information and to apply online, please download the further details and click on the 'apply online' button above.
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