Are you a talented and ambitious computer scientist, computational neuroscientist or machine learner? Are you looking for an opportunity to develop the next generation AI algorithms that are required to realise the third wave of AI? We have a rare opportunity for a Research Associate to work on this problem within the Dept of Computer Science at the University of Sheffield (a world top 100 university) and in collaboration with world-leading neuroscientists in Australia through the prestigious £1.2m EPSRC Centre to Centre project (ActiveAI: active learning and selective attention for rapid, robust and efficient AI).

Your primary role will be to develop a new class of ActiveAI controllers for problems in which insects excel but deep learning methods struggle. These problems have one or more of the following characteristics: (i) learning must occur rapidly, (ii) learning samples are few or costly, (iii) computational resources are limited, and (iv) the learning problem changes over time. Insects deal with such complex tasks robustly despite limited computational power because learning is an active process emerging from the interaction of evolved brains, bodies and behaviours. Through a virtuous cycle of modelling and experiments, you will develop insect-inspired models, in which behavioural strategies and specialised sensors actively structure sensory input while selective attention drives learning to the most salient information, massively reducing the search space. In this way, we will both advance neuroscience and enable ActiveAI solutions which are efficient in learning and final network configuration, robust to real-world conditions and learn rapidly.

You will work under the supervision of Dr Mike Mangan, and Professors Eleni Vasilaki and James Marshall, within the Department of Computer Science (top five in the UK in the most recent REF) in partnership with UK-based research collaborators in the £4.8m EPSRC Brains on Board Programme Grant. You will also lead a new collaboration with world-leading research partners in Australia (led by Prof Andrew Barron, Macquarie University) through extended secondments.

You should be educated to PhD level (or be close to completion) in a related computational field (or equivalent industrial experience), with excellent skills in computational modelling, ideally the use of neural networks to either model biological systems or to solve complex tasks. Previous experience of GPU computing, embedding models onto robots, verification of models through neuroscientific or behavioural assays, are desirable although not essential, but candidates must have an aptitude for learning or improving these skills.