Senior Scientist, Quantum Sensing; Magnetometry Control

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 dynamic team comprising 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 focused 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, ultrastable, 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. You will be taking your code and algorithms and applying them to actual field-deployed platforms, with a focus on real-world performance.

Additionally, you will contribute to the efficient and effective functioning of Q-CTRL, which may involve assisting beyond boundaries of responsibility to help achieve our mission of making quantum technology useful.

• Be part of an established and growing team focused on quantum sensing for industries such as aerospace, defence, geophysical exploration and Earth observation
• Develop quantum control algorithms to improve the sensitivity and robustness of optically pumped magnetometers
• Use statistical techniques such as Kalman and particle filters to improve sensitivity and remove noise
• Perform time series analysis and develop platform and environmental noise compensation techniques using state-of-the-art digital signal processing and machine learning methods
• Use data analytics to translate quantum sensor data streams into capabilities that solve pressing challenges for end-users and customers
• Develop, document, and review high-quality software using Python
• Share 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)
• Share insights into control-augmented quantum sensing 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 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
• Experience in time and frequency digital signal processing, e.g. adaptive filtering, Kalman filtering
• Contributing to a software system architecture with multiple components developed by different teams, adhering to best-practice software development precepts

• Expertise with Python programming language for scientific computing
• Experience with version control e.g. Git with CI/CD pipelines
• 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…