Research Associate in Artificial Intelligence for Autonomous Vehicles

Fixed term contract - 2 years

Full time - 35 hours per week

The opportunity:

This post will be part of the Centre for AI for Assistive Autonomy, a mission-driven research centre established using a UKRI Turing AI World Leading Researcher Fellowship awarded to Prof Subramanian Ramamoorthy. 

The post-holder will be the lead researcher anchoring laboratory activities in the area of Artificial Intelligence for autonomous vehicles, with a focus on human-centred settings including shared autonomy and autonomous operation in crowded urban environments. This represents one of the main application themes within the centre, alongside assistive technology in healthcare domains. This work benefits from parallel work of a foundations team, whose focus will be on fundamental investigations into generative modelling of the full stack of human behaviour, from sensorimotor control to higher level cognitive decision making, to enable the development of person-centred and teachable autonomous systems. 

It is also expected that the post holder will contribute to the development of new assets in the form of experimental robotic systems and demonstrators. These will be expected to make use of data and model assets created by other Centre researchers.

Your skills and attributes for success: 

Essential:

• A PhD (or near completion) in AI and/or robotics, with a focus on autonomous vehicles applications 
• An excellent track record of publications in top-tier journals (e.g. IEEE Trans. or broad-interest science venues) and/or conferences in AI (e.g. IJCAI, AAAI, NeurIPS, ICML, ICLR), robotics (e.g. R:SS, CoRL, ICRA), or allied areas
• Excellent proven experience of developing field robotics systems 
• Ability to work effectively as part of a team, to meet deadlines, and to report on project progress
• Ability to communicate complex information clearly, orally and in writing.

Desirable:

• Research experience with methods for learning and decision making in sequential decision making problems with humans in the loop
• Research experience with real-time perception and scene understanding methods based on state-of-the-art machine learning methods
• Hands-on experience of designing and conducting field experiments, including human subject studies 

Demonstrated experience of the development and management of projects involving multiple stakeholders.

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