Research Associate in Machine Learning for Next-Generation Hardware

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

Benefits offered by this job

41 days annual leave

Hybrid working opportunities

Generous pension

Staff networks

Development opportunities

Job summary

University of Sheffield is seeking a Research Associate to advance nanoscale magnetic neuromorphic computing for energy‑efficient AI. You will work across ML and materials science, using diffusion‑based models to simulate devices and guide inverse design for real‑world tasks such as smart prosthetics and brain–computer interfaces.

You will collaborate with the project team, publish findings, and contribute to the Centre for Machine Intelligence, while developing new computational approaches and

Qualifications

• PhD in Computer Science or Physics or an equivalent discipline; strong research background.
• Proficiency in Python and modern ML libraries (PyTorch, TensorFlow, NumPy, SciPy).
• Experience training and evaluating ML models and computational modelling.
• Excellent written and verbal communication; ability to report findings in high‑impact venues.
• Knowledge of engineering mathematics and the ability to learn new mathematical tools.

Responsibilities

• Utilise and extend diffusion‑based generative models to simulate nanoscale magnetic systems.
• Develop metrics and properties for neuromorphic systems to identify network components.
• Explore heterogeneous neural networks and deploy on real‑world tasks like brain‑computer interfaces.
• Collaborate with academics and partners to train models of physical systems and evaluate across tasks.
• Communicate research findings through presentations and publications.

Skills

Python

Machine Learning

Scientific computing

Git

PyTorch

Education

PhD in Computer Science or Physics (or equivalent)

Tools

NumPy/SciPy

Job description

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

• 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 (desirable).

Further Information

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

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 COM‑[e-mail]

Disability Confident Leader: We are a Disability Confident Leader. 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.

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

Contact

For informal enquiries about this job contact Dr Matt Ellis, project lead, at [e-mail]