Sensors, AI & Computer Vision Specialist (KTP Associate) Ref 4946
School/department: School of Engineering and Informatics, Engineering department
Hours: Full time hours of 37.5 per week
Requests for flexible working options will be considered (subject to business need).
Contract: fixed term for 2 years
Salary: starting at £33,797 to £44,045 per annum, pro rata if part time
Placed on: 9 February 2021
Closing date: 9 March 2021. Applications must be received by midnight of the closing date.
Expected start date: As soon as possible
This post was recently posted on 24 November 2020 – Previous Applicants need not apply.
Looking for an exciting new career in sensor technology, AI and computer vision?
The University of Sussex has teamed up with world leading specialists in entrance security, Gunnebo Entrance Control Ltd, to offer this Sensors, AI & Computer Vision Specialist position based in their office at Maresfield, East Sussex.
Gunnebo Entrance Control is the world's leading supplier of smart security gates. These gates are used in underground stations, offices and airports. They are used to provide security and safety to commuters, office workers and others.
This project will use recent developments in AI and computer vision to improve the operation of the gates, for example the tracking of a person could be used to improve the speed of operation. Multi-modal sensor fusion can be used to improve the accuracy of gates and detect cheating such as tailgating.
To do this, the successful candidate will investigate new sensor modalities and AI techniques, such as, deep learning computer vision and data fusion methods. These will be then integrated on an embedded platform. The project will involve a full cycle of building a machine learning product. This will be from the data capture design, model design and training, to the evaluation and final real-time implementation on hardware. Having hands on python and AI related libraries such as TensorFlow, Keras or Pytorch and general scientific computing and visualization libraries such as numpy, scipy, pandas, matplotlib, OpenCV is crucial. Data manipulation, preprocessing and preparation of data for ML training using python libraries is an important part of this project. At the end of this project, you will get a fantastic commercial experience in the domain of AI and deep learning. The candidate will also be exposed to the business development of the product.
This 24-month project is funded through a Knowledge Transfer Partnership (KTP) award, a UK Government scheme intended to promote beneficial relationships between universities and industry. The role offers a £33k-£44k pa salary and in addition, as a KTP Associate you will receive extensive practical and formal training, gain marketable skills, broaden knowledge and expertise within an industrially relevant project, and be supported by both industrial and academic mentors. The KTP associate will benefit from a tax free Personal Development Budget of £4,667. Academic publications will be produced and there is the likelihood of a permanent position at Gunnebo at the end of the contract.
If you're a talented individual with experience in sensor technology, AI and machine learning looking for a new challenge and to take control of your own project, then make sure you take advantage of this rare opportunity.
Please contact Dr Phil Birch, firstname.lastname@example.org for informal enquiries.
The University of Sussex values the diversity of its staff and students and we welcome applicants from all backgrounds.
How to apply
Download our academic application form [DOC 199.50KB] and Personal details and equal opportunties form [DOC 108.50KB] and fill in all sections.
Email your completed application, and personal details and equal opportunities form, to email@example.com
You should attach your application form and all documents to the email in PDF format (we are unable to accept applications as google.docs or .pages) and use the format job reference number / job title / your name in the subject line.
You can also send your application by post to Human Resources Division, Sussex House, University of Sussex, Falmer, Brighton, BN1 9RH.