Research Associate in Machine Learning for Next-Generation Hardware

Diversity Dashboard; Sheffield; On-site; GBP 38,784 - 39,906; Full time

Benefits offered by this job

Hybrid working

Generous pension scheme

Staff discounts

Job summary

The University of Sheffield is seeking a Research Associate to join the School of Computer Science on an EPSRC-funded project. You will bridge machine learning and materials science to develop brain-like hardware using nanoscale magnetic systems.

You will use diffusion-based generative models to optimise networks, train models of physical systems and publish your findings as part of a ground-breaking research program.

Qualifications

• Background in machine learning or computational modelling.
• Interest in developing novel, unconventional computing systems.
• Excellent collaboration and communication skills.

Responsibilities

• Utilise and expand diffusion-based generative models to simulate nanomagnetic systems within the group framework.
• Develop metrics and properties for neuromorphic networks to identify potential components.
• Explore heterogeneous neural networks and deploy them on real-world tasks such as brain–computer interfaces.
• Collaborate with academics and partners to train models of physical systems and evaluate their properties.
• Communicate findings through presentations and publications at local and international levels.
• Engage with the University research community, including the Centre for Machine Intelligence.

Skills

Machine learning

Computational modelling

Job description

The University of Sheffield is a remarkable place to work. Our people are at the heart of everything we do. Their diverse backgrounds, abilities and beliefs make Sheffield a world-class university.

We offer a fantastic range of benefits including a highly competitive annual leave entitlement (with the ability to purchase more), a generous pensions scheme, flexible working opportunities, a commitment to your development and wellbeing, a wide range of retail discounts, and much more. Find out more about our benefits (opens in a new window) and join us to become part of something special.

Overview

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.

Main duties and responsibilities

• Utilise and expand diffusion-based generative models to simulate a range of nanomagnetic systems within the framework previously developed within the group.
• Develop methods for computing task-independent metrics and properties of neuromorphic systems to determine potential components for networks.
• Explore how devices can be combined as heterogeneous neural networks with advanced computational properties and deploy them on challenging real-world tasks, such as brain-computer interfaces or smart prosthetics.
• 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 levels 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.

Person Specification

Our diverse community of staff and students recognises the unique abilities, backgrounds, and beliefs of all. We foster a culture where everyone feels they belong and are respected. Even if your past experience doesn't match perfectly with this role's criteria, your contribution is valuable, and we encourage you to apply.

Further Information

• Grade: Grade 7
• Salary: £38,784 - £39,906
• Work arrangement: Full-time
• Duration: 14th September 2026 to 31st December 2027, with the potential for further extension to June 2028.
• Line manager: Senior Lecturer in Machine Learning (project lead)
• Direct reports: None
• Right to work in the UK: If you do not currently hold the right to work in the UK, you can find more information here to help determine your visa eligibility. Additional guidance is also available on the UK Visa & Immigration website.
• Our website: sheffield.ac.uk/cs
• For informal enquiries about this job contact Dr Matt Ellis, project lead, at [e-mail]

Next steps in the recruitment process

It is anticipated that the selection process will take place in late August / early September. This will consist of a presentation and interview. We plan to let candidates know if they have progressed to the selection stage within two weeks of applications closing. If you need any support, equipment or adjustments to enable you to participate in any element of the recruitment process you can contact [e-mail]

Our vision and strategic plan

We are the University of Sheffield. This is our vision: sheffield.ac.uk/vision (opens in new window).

What we offer

• A minimum of 41days annual leave, including bank holidays and closure days (pro rata), with the ability to purchase more.
• Flexible working opportunities, including hybrid working for some roles.
• Generous pension scheme.
• A wide range of discounts and rewards on shopping, eating out and travel.
• A variety of staff networks, providing opportunities for social interaction, peer support and personal development (for example, Race Equality, LGBT+, Women's and Parent's networks).
• Recognition Awards to reward staff who go above and beyond in their role.
• A commitment to your development access to learning and mentoring schemes.
• A range of generous family-friendly policies:
  • paid time off for parenting and caring emergencies
  • access to menopause support in the workplace
  • paid time off and support for fertility treatment
  • and more

We are a Disability Confident Leader (opens in new window). If you have a disability and meet the essential criteria for this job you will be invited to take part in the next stage of the selection process.

We are a research university with a global reputation for excellence. Our ideas and expertise change the world for the better, making a real difference to society. We know that when people come together with different views, approaches and insights it can lead to richer, more creative and innovative teaching and research and the highest levels of student experience. Our University Vision ( www.sheffield.ac.uk/vision ) outlines our commitment to building a diverse community of staff and students that recognises and values the abilities, backgrounds, beliefs and ways of living for everyone.