Description
Research Assistant / Research Associate in Energy-Efficient AI, Spiking Neural Networks and Neuromorphic Computing
Job number
ENG03859
Faculties
Faculty of Engineering
Departments
Department of Electrical and Electronic Engineering
Salary or Salary range
£43,863- £57,472 per annum
Location/campus
South Kensington Campus - Hybrid
Contract type work pattern
Full time - Fixed term
Posting End Date
15 May 2026
About the role
We are seeking a Research Assistant (a Masters graduate wanting to undertake a PhD) or Research Associate (a PhD graduate wanting to undertake a postdoc). Our aim is to explore new approaches to energy-efficient artificial intelligence based on temporal neural computation. The project investigates how spiking neural networks, temporal coding, and learned neural delays can enable accurate computation with extremely low-precision weights, potentially unlocking 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 contribute to developing neural architectures in which time (spike timing and delays) replaces numerical precision and memory movement as the key computational resource. The project will combine algorithm development with hardware-aware modelling and evaluation on FPGA-based platforms. The successful candidate will have the opportunity to help define new approaches to temporal neural computation and energy-efficient AI.
This role provides an exciting opportunity to work on fundamental questions in neuromorphic and energy-efficient AI, while developing techniques that could translate into 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 the post-holder access to a rich network of industrial and academic collaborators and routes to direct impact.
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
In this role, you will investigate neural architectures that exploit temporal coding and learned delays to enable efficient computation on digital hardware. You will develop and evaluate spiking neural network models, explore training methods for delay-based computation, and analyse how temporal representations trade off with weight precision and memory usage.
You will primarily be collaborating with three Imperial College academics: Prof Christos Bouganis, Prof George A. Constantinides and Dr Dan Goodman, who each bring leading expertise relevant to this research programme.
You will implement models in software frameworks for neural simulation and machine learning, and work with hardware-aware efficiency metrics to evaluate energy, memory, and latency trade-offs. The project will also involve exploring how these architectures map to digital hardware platforms, including FPGA-based systems.
You will contribute to research publications, present results at conferences, and help develop new approaches to algorithm–hardware co-design for energy-efficient AI.
You will contribute to the broader Innovation and Knowledge Centre mission to help bridge the gaps between academic research and industrial impact in this field.
What we are looking for
A strong background in machine learning, computer engineering, applied mathematics, computational neuroscience or a closely related field.
Experience with software engineering for scientific computing or machine learning (e.g. PyTorch), digital hardware design (e.g. Verilog) and mathematical maturity.
An interest in one or more of the following areas:
neuromorphic computing
machine learning for efficient AI
digital hardware or FPGA architectures
computational neuroscience
Ability to analyse complex systems, develop new models, and communicate research clearly.
A motivated and collaborative researcher with enthusiasm for advancing the foundations of energy-efficient AI.
Research Associate: Hold a PhD in machine learning, computer engineering, applied mathematics, computational neuroscience, or a closely related discipline, or equivalent research, industrial or commercial experience.
Research Assistant: A master’s degree (or equivalent) in machine learning, engineering, applied mathematics, computer science, or a closely related discipline, or equivalent research, industrial or commercial experience.
Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant.
What we can offer you
The opportunity to work on cutting-edge research in energy-efficient AI and neuromorphic computing, addressing fundamental challenges in how neural systems compute using time and sparse events.
The chance to join a highly active research environment within the Department of Electrical and Electronic Engineering at Imperial College London, collaborating with experts in machine learning, digital hardware design, and computational neuroscience.
Hands-on experience with algorithm–hardware co-design, including neural modelling, efficient machine learning methods, and FPGA-based hardware platforms.
The opportunity to develop research publications and contribute to emerging directions in ultra-low-power AI technologies.
Grow your career: Gain access to Imperial’s sector-leading career development support for researchers, including training, mentoring, and opportunities for progression.
A competitive salary and sector-leading benefits package (including 39 days leave per year and generous pension schemes).
Further information
This is a fixed-term position 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.
Pre-doctoral candidates are welcome and will have the opportunity to register for a PhD during the course of the appointment.
If you have questions about the role please contact:
Prof George A. Constantinides – g.constantinides@imperial.ac.uk
Please note that job descriptions are not exhaustive, and you may be asked to take on additional duties aligned with the key responsibilities described above.
If you encounter any technical issues while applying online, please email support.jobs@imperial.ac.uk
