Object Classification and Threat Identification Expert, KTP Associate

Updated: 2 months ago
Location: Cranfield, ENGLAND
Job Type: FullTime
Deadline: 16 Nov 2021

School/Department School of Aerospace, Transport and Manufacturing
Based at Off Campus
Hours of work 40 hours per week, normally worked Monday to Friday. Flexible working will be considered.
Contract type Fixed term contract
Fixed Term Period 24 months
Salary £40,000 to £50,000 per annum, dependent upon qualifications and experience (40 hours per week), plus £4,000 personal development budget
Apply by 16/11/2021

Role Description

About Aerospace / Operational Solutions Ltd.

Cranfield Aerospace is one of the eight themes at Cranfield University offering world-class and niche post-graduate level research, education, training and consultancy. Cranfield is the number one university in Europe for aerospace. We are one of the few universities in the world to have its own airport.

The Centre for Autonomous and Cyber-Physical Systems at Cranfield has a leading reputation in autonomous and space systems, established with over 15 years of research in this field. We cover all types of autonomous systems including airborne, ground, and marine as well as autonomous space exploration.

We are renowned for being a leading European centre for postgraduate teaching and research in autonomous and space systems with approximately 500 alumni members around the world working in the space industry. Our research draws on staff expertise and industry collaborations. A significant body of research in ISTAR applications, including multiple target tracking, sensor and data fusion, and information fusion, has been developed over the last two decades.

You will be based at Reading where OSL is located, and will work closely with Mark Lupton, Chief Technology Officer, and the technical team at OSL, and a team of academics at the Cranfield University, especially in Centre for Autonomous and Cyberphysical Systems.

About the Role

This project will embed the knowledge and capabilities within Operational Solutions Ltd to deliver novel solutions to assimilate, process and analyse complex data from multiple, dissimilar sensors to provide world-leading solutions against Unmanned Aerial Vehicle disruption. Your role will lead the development of innovative high performance object classification and threat identification modules, addressing performance improvement based on data from various complementing sensors.

About You

This role requires knowledge of Target Tracking, especially Object Classification and Threat Identification. Knowledge of Machine Learning (with application to radar, vison sensors such as EO/IR and camera sensors, audio sensors, sensor network or equivalent) would also be desirable in this role. This role would suit an individual with excellent communication skills, high degree of personal motivation, and the ability to independently work. Previous experience of Target/Object Classification and Identification is essential. You will hold a MSc level degree in a classification and identification related subject, e.g., aerospace engineering, electronic engineering, or related fields, or preferably, a PhD in one of these areas.

In return, the successful applicant will have exciting opportunities for career development in this key position, and to be at the forefront of world leading research and education, joining a supportive team and environment.

Our Values and Commitments

Our shared, stated values help to define who we are and underpin everything we do: Ambition; Impact; Respect; and Community. Find out more here .

We aim to create and maintain a culture in which everyone can work and study together and realise their full potential. Find out about our key commitments to Equality, Diversity and Inclusion and Flexible Working here . We are currently piloting hybrid working arrangements until April 2022.  This means the majority of our staff are spending between 40% and 60% of their time working from the office where job roles allow.

How to apply

For an informal discussion about this opportunity, please contact Hyo-Sang Shin, Professor of Guidance, Navigation and Control, on (T): +44 (0)1234 75 8577 or +44 (0)1234 750111 Ext. 8577 or (E): h.shin@cranfield.ac.uk

This partnership received financial support from the Knowledge Transfer Partnerships (KTP) programme. KTP aims to help businesses to improve their competitiveness and productivity through the better use of knowledge, technology and skills that reside within the UK knowledge base. This successful Knowledge Transfer Partnership project, funded by UK Research and Innovation through Innovate UK, is part of the government’s Industrial Strategy.


View or Apply

Similar Positions