AI-Ready Photonic Chips with Built-In Quantum Thermometry in Teddington

Photonic Integrated Circuits (PICs) are emerging as a transformative platform for next-generation artificial intelligence (AI) hardware, enabling ultrafast, energy-efficient optical data processing. However, as PICs scale to the densities required for AI acceleration, thermal effects become a critical bottleneck, limiting performance, reliability and efficiency. This challenge presents a global sustainability problem since data infrastructure is projected to consume over 20% of worldwide energy within a decade. Tackling this demands a step change in how temperature is measured and controlled directly on-chip. This PhD will pioneer a new approach to nanoscale temperature sensing, combining photonics with quantum-enabled metrology to deliver accurate, calibration-resilient measurements within densely integrated PICs. Existing techniques suffer from drift and lack the spatial resolution needed for sub-micron devices. You will develop a fundamentally new class of on-chip thermometry that overcomes both limitations. The project spans the full research and development pipeline from physics to nanoscale engineering; from modelling and design through to fabrication and experimental validation.

Working in collaboration with the UK’s National Physical Laboratory (NPL), you will realise and test fully integrated photonic sensors that incorporate light sources, resonant thermometers and detectors on a single chip. This is a unique opportunity to work at the cutting-edge of AI hardware, photonic integration and quantum metrology, contributing directly to the development of scalable low-carbon computing technologies. You will work within the Electronics and Nanoscale Engineering (ENE) Division and will have access to the world leading facilities within the James Watt Nanofabrication Centre at the University of Glasgow. The project is a collaboration between the University of Glasgow and our partners at the National Physical Laboratory, the UK’s centre of excellence for metrology, where some of the research will be conducted.

The candidate will have a degree in Physics, Electrical/Electronic Engineering or related experimental science at the 2:1 level or above. They will have demonstrable interest in semi-conductor physics, devices, nano-fabrication and device characterisation. Full training will be given in device fabrication and characterisation.

The National Physical Laboratory (NPL) is a world-leading centre of excellence that provides cutting-edge measurement science, engineering and technology to underpin prosperity and quality of life in the UK. NPL and DSIT have strong commitments to diversity and equality of opportunity, and welcome applications from candidates irrespective of their background, gender, race, sexual orientation, religion, or age, providing they meet the required criteria. Applications from women, disabled and black, Asian and minority ethnic candidates in particular are encouraged. All disabled candidates (as defined by the Equality Act 2010) who satisfy the minimum criteria for the role will be guaranteed an interview under the Disability Confident Scheme.

At NPL, we believe our success is a result of the diversity and talent of our people. We strive to nurture and respect individuals to ensure everyone feels valued by treating everyone on the basis of their own individual merits and abilities regardless of their own or perceived identity. As part of our commitment to diversity & inclusion, we ensure we’re creating an environment where all our colleagues feel supported and welcome. We are committed to the health and well-being of our employees. Flexible working and social activities are embedded in our culture to create a positive work-life balance, along with a broad range of rewards, benefits and recognition. Our values are at the heart of what we do, and they shape the way we interact, develop our people and celebrate success. To ensure everyone has an equal chance, we’re always willing to make reasonable adjustments to the recruitment process.