Research Assistant / Research Associate in Energy-Efficient AI, Spiking Neural Networks and Neuromorphic Computing

About the role

We are seeking a Research Assistant or Research Associate to work on new approaches to energy-efficient artificial intelligence based on temporal neural computation. The project will investigate how spiking neural networks, temporal coding and learned neural delays can enable accurate computation with extremely low-precision weights, potentially supporting a new generation of ultra-efficient AI hardware.

Working at the intersection of machine learning, computational neuroscience and digital hardware design, the successful candidate will help develop neural architectures in which time becomes a key computational resource. The project combines algorithm development, hardware-aware modelling and evaluation on FPGA-based platforms.

This is an opportunity to contribute to fundamental research in neuromorphic and energy-efficient AI while developing techniques relevant to future AI accelerators and edge-AI technologies. The role will be affiliated with NeuroWare, the new national Innovation and Knowledge Centre in Neuromorphic Computation, providing access to a strong network of academic and industrial collaborators.

Pre-doctoral candidates are strongly encouraged to apply. Candidates appointed as Research Assistant will have the opportunity to register for a PhD during the appointment, subject to standard university procedures.

What you would be doing

You will investigate neural architectures that exploit temporal coding and learned delays for efficient computation on digital hardware. You will develop and evaluate spiking neural network models, explore training methods for delay-based computation, and analyse trade-offs between temporal representation, weight precision and memory use.

You will work primarily with Prof Christos Bouganis, Prof George A. Constantinides and Dr Dan Goodman.

You will implement models using machine learning and neural simulation frameworks, evaluate them using hardware-aware efficiency metrics, and explore how they map onto digital hardware platforms including FPGAs. You will also contribute to publications, present results at conferences, and support the wider mission of the Innovation and Knowledge Centre.

What we are looking for

You should have a strong background in machine learning, computer engineering, applied mathematics, computational neuroscience, or a closely related field.

We are looking for someone with:

• experience in scientific computing or machine learning software, digital hardware design, and strong mathematical ability
• interest in neuromorphic computing, efficient AI, digital hardware or FPGA architectures, and/or computational neuroscience
• the ability to analyse complex systems, develop new models, and communicate research clearly
• a collaborative and motivated approach to research

For Research Associate, you should hold a PhD in a relevant discipline, or have equivalent research, industrial or commercial experience. For Research Assistant, you should hold a Master's degree in a relevant discipline, or have equivalent experience. Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant.

Further information

This is a fixed-term post for up to 36 months, subject to probation, with an expected start date in early October 2026. The post is based in the Department of Electrical and Electronic Engineering at Imperial College London.

For informal enquiries, please contact Prof George A. Constantinides at g.constantinides@imperial.ac.uk

Closing date: 15/05/2026

Advert information