- Location
- Sydney · Oxford
- Workplace
- Hybrid
- Type
- Full-time
- Department
- Research
- Seniority
- Senior
- Education
- PhD
- Source
- Lever
Description
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 modeling, and modern Bayesian estimation and signal processing techniques.
The Quantum Sensing Research and Quantum Sensing R&D 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.
About the role
This opportunity will focus on developing 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. 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.
Skills
PythonCI/CDMachine LearningGit