Research Associate in Machine Learning for Next-Generation Hardware


Company:	University of Sheffield
Salary:	Not specified
Hours:	Full-time
Location:	Sheffield, S10 2TN
Job type:	Temporary
Posting date:	22 Jul 2026
Closing date:	21 Aug 2026

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Summary

University of Sheffield

Job Reference Number:

2855

Faculty:

Faculty of Engineering

School:

School of Computer Science

Closing Date:

23rd August 2026

As AI systems scale, their energy consumption is skyrocketing. To tackle this crisis, we need to move beyond traditional computing and look at nanomagnetic devices, which offer unique, ultra-low-energy properties perfect for creating novel, brain-like hardware neural networks.

We have an exciting opportunity to join the School of Computer Science as a research associate for an EPSRC-funded project. You will be part of an interdisciplinary team bridging the gap between machine learning and materials science to develop next-generation computing hardware based on nanoscale magnetic systems. This project aims to explore how systems with complementary properties can be combined to overcome the current limitations of individual elements.

In this role, you will utilise diffusion-based generative models to simulate experimental devices and how they can be combined into heterogeneous networks. These models will allow us to use inverse design techniques to optimise network composition and train them to solve challenging real-world tasks, such as smart prosthetics or brain-computer interfaces.

We are looking for someone with a background in either machine learning or computational modelling and strong interest in developing novel, unconventional computing systems to tackle complex machine learning tasks. 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.

Key responsibilities in the role include:

● Using diffusion-based generative models of physical systems to explore device computation properties.

● Simulating heterogeneous device networks and applying them to challenging, real-world tasks.

● Collaborating with researchers in Materials Science to validate simulated networks against real systems.

● Disseminating research findings through presentations, publications in international journals, and conferences.

We are committed to exploring flexible working opportunities which benefit the individual and University.

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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