Research Associate in Machine Learning for Neuromorphic Spintronics

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.

Key responsibilities in the role include:

• Designing digital twins and evaluating the performance of spintronic neuromorphic systems using advanced machine learning techniques, such as neural ordinary and stochastic differential equations.
• Collaborating with researchers in the Department of Materials Science and other project partners to model physical systems and networks of devices.
• Disseminating research findings through presentations, publications in international journals, and conferences.

We build teams of people from different heritages and lifestyles from across the world, whose talent and contributions complement each other to greatest effect. We believe diversity in all its forms delivers greater impact through research, teaching and student experience.

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