Oxide film devices for AI processing of temporal signals (Ref: PH/PB-SF1/2026)

About the Project

It is possible to run machine learning algorithms to classify, reconstruct and predict future time-dependent input signals from sensors in application areas such as language processing, environmental, engineering, or medical monitoring. However, high energy consumption associated with the state-of-the-art hardware for neural networks is hindering development of mobile, compact sensors that can be operated stand-alone and offline. This is an opportunity to design devices that use fundamentally new physical approaches to processing neural networks in order to tackle this issue.

The goal of this project is experimental development of thin film devices capable of processing time-dependent electrical signals as part of a neural network. You will prepare thin films of oxide materials by magnetron sputtering deposition; employ different characterisation techniques to study the material properties, e.g. x-ray photoelectron spectroscopy; design and build novel electronic devices by UV photolithography; test their performance with respect to the industry standard benchmarks using a probe station and a seminconductor device analyser, realise neural networks for solving different computation tasks, for example, for future signal prediction.

This research project is at the intersection of artificial intelligence and device physics. You will work closely with the supervisors and participate in regular meetings with a team of other academics, research staff and PhD students at Loughborough.

Name of primary supervisor/CDT lead:
Pavel Borisov p.borisov@lboro.ac.uk

Entry requirements:
Applicants should have, or expect to achieve, at least a 2:1 Honours degree (or equivalent) in Physics, Material Science or Engineering or a related subject. A relevant Master’s degree and experience in one or more of the following will be an advantage: first-hand working experience with thin film preparation and characterisation techniques, neural networks, memristors. We would particularly welcome applicants who are good at working as part of a team and interested in cross-disciplinary collaborations.

English language requirements:
Applicants must meet the minimum English language requirements. Further details are available on the International website (http://www.lboro.ac.uk/international/applicants/english/).

Bench fees required: No

Closing date of advert: 1st July 2026

Start date: October 2026

Full-time/part-time availability: Full-time 3 years

Fee band: 2025/26 Band RB (UK £5,006, International £28,600)

How to apply:
All applications should be made online. Under programme name, select Physics. Please quote the advertised reference number: PH/PB-SF1/2026 in your application.
To avoid delays in processing your application, please ensure that you submit a CV and the minimum supporting documents.
The following selection criteria will be used by academic schools to help them make a decision on your application.
Please note that this criteria is used for both funded and self-funded projects. Please note, applications for this project are considered on an ongoing basis once submitted and the project may be withdrawn prior to the application deadline, if a suitable candidate is chosen for the project.

Project search terms:
artificial intelligence, solid state physics, memristors neuromorphic computing neural networks oxides electronic devices