Senior Scientist, Quantum Sensing; Magnetometry Modelling

About the team

Q-CTRL’s Quantum Sensing Research team performs basic and applied research in quantum sensing and quantum‑enabled navigation. It is a highly dynamic team comprising approximately 30 specialists in theoretical and experimental atomic physics, cold‑atom interferometry, navigation modelling, and modern Bayesian estimation and signal processing techniques. The Quantum Sensing Research and Quantum Sensing Engineering teams work closely together, translating novel quantum sensing research into a useful capability and product.

We are focussed on pushing the boundary of what is possible in quantum sensing and ensuring our R&D has real‑world impact.

The purpose of this role is to help develop next‑generation small‑form‑factor quantum magnetometers that are exquisitely sensitive, ultra stable, and able to operate reliably in harsh fielded environments. A key part of this role is to use the techniques of quantum control to improve the sensitivity and robustness of magnetometers. This will involve developing novel software‑layer control protocols and operating modes for optically pumped atomic magnetometers, as well as advanced statistical filtering techniques, in order to deliver step‑change performance improvements in noisy operating conditions.

The algorithms, signal processing, and data analytics developed by you and the rest of the team will inform the design, build, and operation of our quantum sensors. In all cases, a major focus is on real‑world performance – you will be taking your code and algorithms, applying them to actual, field‑deployed platforms, and working to make them effective. Additionally, you will contribute to the efficient and effective functioning of Q‑CTRL.

This will sometimes include helping out regardless of boundaries of responsibility to help us achieve our mission of making quantum technology useful.

• Perform time series analysis and develop platform and environmental noise compensation techniques using state‑of‑the‑art statistical signal processing
• Use statistical techniques such as Kalman and particle filters to improve sensitivity and remove noise
• Develop quantum control algorithms to improve the sensitivity and robustness of optically pumped magnetometers
• Develop, document, and review high‑quality software using Python
• Share your insights and results with the broader Quantum Sensing Division, company, external stakeholders and customers through verbal presentations and written outlets (e.g. academic publications, technical reports, blog posts)
• Other duties within the Employee's skills and experience, or with reasonable training

• A PhD (or equivalent industry experience) in Physics, Engineering or a closely related discipline.
• Experience in time and frequency digital signal processing, e.g. adaptive filtering, statistical filtering
• Experience in the theory, numerical modelling, and/or optimization of one or more of the following: atomic spin ensembles, quantum sensing in warm atomic vapours, pulse‑level optimisation and quantum control techniques.
• Knowledge of statistical techniques such as Kalman and particle filtering

• Microwave and RF signal conditioning experience
• Expertise with Python programming language for scientific computing
• Experience modelling real quantum experiments/hardware and working closely with both theoretical and experimental teams
• Demonstrated experience in improving experimental outcomes through novel signal processing or denoising algorithms

Q‑CTRL is the global leader in AI‑powered quantum control infrastructure software. We build the tools that make quantum technology useful, solving the hardest challenges in quantum computing and quantum sensing to deliver real‑world impact. Founded in 2017, we operate globally with offices in Sydney, Los Angeles, San Francisco, Berlin, and Oxford. Our teams bring together technical and multi‑disciplinary expertise across the product lifecycle, and we’re hiring talent to help scale every part of the business.

We work quickly to turn cutting‑edge science into deployable technology. In 2024 we raised US…