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Overview

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.

Main duties and responsibilities

• Develop machine learning models for a range of nano-magnetic systems based on the neural ordinary/stochastic differential equation framework previously developed within the group.
• Investigate few-shot and meta-learning techniques for rapid training of device models with limited data or parameter variation.
• Develop methods for computing task independent metrics and properties of potential neuromorphic systems to determine potential components for networks.
• Explore how these devices can be combined as heterogeneous networks with advanced computational properties and deploy them on challenging real-world tasks, such as brain-computer interfaces and activity detection.
• 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 level 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.

Essential criteria

• Hold, or be close to completing, a PhD in Computer Science, Physics or in a relevant discipline (or have the equivalent experience).
• Knowledge of computational modelling techniques, such as solving differential equations, or related experience.
• Ability to write up research findings for submission to high-impact peer-reviewed conferences and journals.
• Knowledge of programming and computational modelling using relevant languages (e.g Python, Matlab, C/C++) and tools (e.g git).
• Effective communication skills, both written and verbal, to present findings to project collaborators and conferences.
• Knowledge of engineering mathematics (linear algebra, probability, basic calculus) and the ability to learn new mathematical tools.
• Ability to develop creative approaches to problem solving.
• Ability to work effectively as part of a multidisciplinary research team.
• Ability to assess and organise resources and plan and progress work activities to meet key project deadlines.

Desirable criteria (max 2)

• Experience in developing and deploying machine learning tools.
• Experience of modelling or development of physical computing systems, ideally magnetic/ spintronic devices.

Further Information

Grade: 7

Duration: Fixed term from 01.06.2025 to 31.05.2027

Line manager: Lecturer in Machine Learning

Direct reports: None

For informal enquiries about this job contact
Research Team (Post-Award) on com-researchrecruitment@sheffield.ac.uk

Next steps in the recruitment process

It is anticipated that the selection process will take place within a month of the closing date. This will consist of a presentation and an interview. 

We will let all applicants know whether they have been successful in progressing to the next round. I fyou have any questions about the recruitment process you can contact the Research Recruitment Team at COM-ResearchRecruitment@sheffield.ac.uk

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 41 days annual leave including bank holiday 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; integrated with our Academic Career Pathways.
• A range of generous family-friendly policies
  • paid time off for parenting and caring emergencies
  • support for those going through the menopause
  • paid time off and support for fertility treatment
  • and more

More details can be found on our benefits page: sheffield.ac.uk/jobs/benefits (opens in a new window).

We are a Disability Confident Employer. 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.

Closing Date : 06/04/2025 

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.