We offer an 18-months post-doctoral position (with possible extension) for a motivated theoretical researcher to join our work on the application of machine learning to quantum sensing and imaging. Our goal is to push the limits in detecting individual nuclear spins by utilising advanced signal processing techniques such as adaptive Bayesian estimation and deep learning. We have pioneered the application of Bayesian inference to quantum sensing (see for example these papers: Nature Nano 2016, NJP 2020, NPJ Quantum Info 2021) and we currently operate a unique adaptive setup capable of performing online Bayesian estimation in <100 microseconds. The candidate is expected to design and benchmark algorithms to improve the performance of spin-based quantum sensors, which will be then implemented in our state-of-the-art adaptive quantum sensing facilities. The position is funded by the UK Quantum Technology Hub in Quantum-Enhanced Imaging (Quantic).
Where: the research will be carried out in the Quantum Photonics Lab at Heriot-Watt University (Edinburgh, UK), under the supervision of Dr Cristian Bonato. The work will be carried out in collaboration with Erik Gauger (quantum theory, webpage) and Yoann Altmann (statistical learning and Bayesian inference, webpage ).
Who: we are looking for a theoretical researcher, willing to interact with experiments, with a background in either physics (quantum technology) or computer-science/electrical-engineering.
Please contact Dr Cristian Bonato (c.bonato@hw.ac.uk) for inquiries (as soon as possible).
