Research Associate in Backpropagation-Free Learning for Physical Neural Networks

Research Associate in Backpropagation-Free Learning for Physical Neural Networks

We are recruiting a Research Associate to develop learning algorithms that train physical neural networks without backpropagation. The hardware is nonlinear and analogue: nanomagnetic, nonlinear nanophotonic, and nanoelectronic. You will build forward-only and contrastive learning rules that train these systems in situ, and the physics-aware models needed to develop them. You will lead the algorithmic direction of the project.

What you would be doing

• Design, implement and benchmark backpropagation-free training algorithms for nonlinear physical neural networks: contrastive learning, forward-forward and self-contrastive rules, physical Kolmogorov-Arnold networks, and related architectures.
• Port these algorithms across three hardware substrates (nanomagnetic, nonlinear nanophotonic, nanoelectronic) and find the substrate-independent principles that make forward-only learning work on real devices.
• Build physics-aware differentiable surrogate and digital-twin models, including neural ODEs, to develop and stress-test learning rules alongside experiments.
• Develop noise-aware and hardware-aware training that closes the sim-to-real gap on stochastic, drifting devices.
• Publish in leading venues, present internationally, and help win follow-on funding

What we are looking for

• A PhD (awarded or imminent) in physics, engineering, computer science, applied maths, or a closely related field.
• Original work on backpropagation-free learning. Ideally you are first author on a novel learning algorithm.
• Published experience across nanomagnetic, nonlinear nanophotonic, and/or nanoelectronic hardware.
• Hands-on experience with physical Kolmogorov-Arnold networks and other emerging non-MLP architectures.
• Physics-aware surrogate and digital-twin models, including neural ODEs, and noise-aware training for stochastic analogue devices.
• Expert scientific Python (PyTorch or JAX) and a record of reproducible, released code.

What we can offer you

• Lead the algorithmic core of an ambitious physical-computing programme spanning nanomagnetism, nanophotonics and nanoelectronics, within an international collaboration.
• The opportunity to continue your career at a world-leading institution and be part of our mission to continue science for humanity.
• As a member of research staff you have 10 development days to use to develop your skills and explore your career prospects
• Sector-leading salary and remuneration package (including 43 days off a year and generous pension schemes).
• Be part of a diverse, inclusive and collaborative work culture with various staff networks and resources to support your personal and professional wellbeing .

Further information

Please note that this is a PhD level role but candidates who have not yet been officially awarded will be appointed as a Research Assistant.

The successful candidate is expected to start from 1 st October 2026.

The post is in Dr Jack Gartside's group in the Department of Physics, which works on physical neuromorphic computing across nanomagnetic, nanophotonic and nanoelectronic hardware.

This is a full-time post (35 hours per week).

This role is for a fixed-term contract for 24 months.

If you require any further details about the role, please contact: Dr Jack Gartside,

Please note that job descriptions are not exhaustive, and you may be asked to take on additional duties that align with the key responsibilities mentioned above.

If you experience any technical issues while applying online, please don't hesitate to email us at . We're here to help.

Attached documents are available under links. Clicking a document link will initialize its download.

Please note that job descriptions are not exhaustive, and you may be asked to take on additional duties that align with the key responsibilities mentioned above.

We reserve the right to close the advert before the stated closing date, should we receive a high volume of applications. It is therefore advisable that you submit your application as early as possible to avoid disappointment.

If you encounter any technical issues while applying online, please don't hesitate to email us . We're here to help.

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