Job Description
Job Title: Research Associate in Machine Learning for Neuromorphic Spintronics
Posting Start Date: 11/03/2025
Job Id: 821
School/Department: Computer Science
Work Arrangement: Full Time (Hybrid)
Contract Type: Fixed-term
Salary per annum (£): £37,999
Closing Date: 06/04/2025
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OVERVIEW

Inspired by the human brain, neuromorphic computing aims to tackle the growing energy demand of AI-based systems. We have an exciting opportunity to join the School of Computer Science as part of an interdisciplinary team developing the next-generation computing hardware based on nanoscale magnetic systems. We are recruiting a Research Associate to join an EPSRC-funded project that aims to explore how different systems with complementary properties can be combined to overcome current limitations. To do this we will develop digital twins of experimental magnetic devices, which will be used to evaluate computational properties and train heterogeneous networks of devices applied on challenging real-world tasks. This post will develop state-of-the-art machine learning models to develop and demonstrate this approach on tasks such as a smart prosthetic and multi-modal human activity recognition.
Successful candidates will contribute to ground-breaking research that has the potential to significantly reduce the energy consumption of AI systems and accelerate advancements in the field. This is an exciting opportunity to work at the intersection of machine learning and materials science. We are looking for someone with a strong interest in developing novel, unconventional computing systems to tackle complex machine learning tasks at low energy. You should hold a PhD, or be close to submitting, in a science or engineering discipline with a particular background in either machine learning or computational modelling.

• Develop machine learning models for a range of nano-magnetic systems based on the neural ordinary/stochastic differential equation framework previously developed within the group.
• Investigate few-shot and meta-learning techniques for rapid training of device models with limited data or parameter variation.
• Develop methods for computing task independent metrics and properties of potential neuromorphic systems to determine potential components for networks.
• Explore how these devices can be combined as heterogeneous networks with advanced computational properties and deploy them on challenging real-world tasks, such as brain-computer interfaces and activity detection.
• Collaborate with the project team (academics and fellow research associate) and partners to train models of physical systems and evaluate their properties over a range of task independent qualities.
• Communicate research findings at the local and international level through presentations and publications.
• Engage with the research community within the University, including the Centre for Machine Intelligence.
• Keep up to date on relevant work in the field (reading and reviewing literature as appropriate).
• Work closely with the team, attend project meetings, and use collaborative tools like Google Meet, git, etc.
• Carry out other duties, commensurate with the grade and remit of the post