Research Associate in Sensor Data and Resource Management for Autonomous Airborne Platforms

Fixed Term up to 24 months

Full time 35 Hours per week

Number of posts 1

The Opportunity:

This is an exciting opportunity for the Research Associate to contribute to the EPSRC Smart Products Made Smarter prosperity partnership within the Institute of Imaging, Data, and Communications at the University of Edinburgh, which is funded by EPSRC and in collaboration with colleagues at Heriot-Watt University and Leonardo.

The successful candidate will work on novel techniques and verifiable frameworks for sensor task management and distributed sensing on autonomous airborne platforms. Solutions will build upon data captured and fused from radar and electro-optic and infra-red (EOIR) sensors on single or multiple platforms. Distributed sensor networks and distributed multi-sensor fusion techniques address key challenges in intelligence, surveillance, target acquisition, and reconnaissance, and has timely and unique applications across a range of defence and civilian applications, including search and rescue, law enforcement, and remote sensing.

This role is UE07 and is available on a fixed term basis for up to 24 months.

A full list of main responsibilities and the knowledge, skills and experience required for the role can be found in the full job description below.

Your skills and attributes for success: 

• Proven track record of research in signal processing, communications, machine learning, or mathematical subjects related to sensor management, both theoretical and implementational.
• Experience of signal processing challenges in multi-target, multi-sensor, distributed sensing, and processing. 
• Background in numerical Bayesian inference, with expertise in sequential Monte Carlo methods (such as particle filtering) or other sequential filtering techniques (such as Kalman filtering, UKF, EKF etc). 
• Knowledge of software development in Python, MATLAB, C++, Java or equivalent.
• Experience in delivering research project results in a signal processing and communications, machine learning, or related context; i.e. a record of peer-reviewed journal and conference papers in a relevant area.