Research Associate in Machine Learning for Next-Generation Hardware

The University of Sheffield; Sheffield; On-site; GBP 38,784 - 39,906; Full time

Benefits offered by this job

Annual leave

Pensions

Flexible working

Discounts

Development and wellbeing

Job summary

The University of Sheffield's School of Computer Science is inviting applications for a Research Associate on an EPSRC-funded project. You will join an interdisciplinary team bridging machine learning and materials science to develop next-generation computing hardware based on nanoscale magnetic systems.

You will utilise diffusion-based generative models to simulate devices and optimise heterogeneous networks for real-world tasks such as brain-computer interfaces.

Qualifications

• Hold, or be close to completing, a PhD in Computer Science, Physics or a relevant discipline (or have the equivalent experience).
• Knowledge of computational modelling and machine learning techniques, with practical experience training and evaluating models.
• Proficiency in Python, or similar, and experience with modern machine learning/scientific libraries (e.g., PyTorch, TensorFlow, NumPy, SciPy).
• Excellent written and verbal communication skills, with a proven ability to write up research findings for high‑impact peer‑reviewed journals/conferences and present to multidisciplinary teams.
• Knowledge of engineering mathematics (linear algebra, probability, basic calculus) and the ability to learn new mathematical tools.
• Ability to work effectively as part of a multidisciplinary research team.
• Ability to manage own research workflow, organise resources, and progress work activities independently to meet project deadlines.
• Experience in modelling or developing physical computing systems, ideally nanoscale magnetic, spintronic, or neuromorphic devices.
• Knowledge of advanced computational modelling techniques, such as solving differential equations or dynamical systems simulation.

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.

Skills

PhD in CS/Physics

Python

ML libraries

Communication skills

Engineering mathematics

Multidisciplinary teamwork

Research workflow

Physical computing systems

Nanoscale devices

Education

PhD in CS/Physics

Tools

PyTorch

TensorFlow

NumPy

SciPy

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. Please ensure that you reference the application criteria in the application statement when you apply.

Criteria

Essential Or Desirable

Stage(s) assessed at

Hold, or be close to completing, a PhD in Computer Science, Physics or a relevant discipline (or have the equivalent experience).

Essential Application
Knowledge of computational modelling and machine learning techniques, with practical experience training and evaluating models.

Essential Application/interview
Proficiency in Python, or similar, and experience with modern machine learning/scientific libraries (e.g., PyTorch, TensorFlow, NumPy, SciPy).

Essential Application/interview
Excellent written and verbal communication skills, with a proven ability to write up research findings for high‑impact peer‑reviewed journals/conferences and present to multidisciplinary teams.

Essential Application/interview
Knowledge of engineering mathematics (linear algebra, probability, basic calculus) and the ability to learn new mathematical tools.

Essential Application/interview
Ability to work effectively as part of a multidisciplinary research team.

Essential Application/interview
Ability to manage own research workflow, organise resources, and progress work activities independently to meet project deadlines.

Essential Application/interview
Experience in modelling or developing physical computing systems, ideally nanoscale magnetic, spintronic, or neuromorphic devices.

Desirable
Application/interview
Knowledge of advanced computational modelling techniques, such as solving differential equations or dynamical systems simulation.

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

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 you can contact [e-mail]

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.

What We Offer

• A minimum of 41 days 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