MARS Senior Research Associate in Machine Learning to Improve Sensing in Quantum Gases

• Develop and implement data-driven machine learning methods to design, control, and interpret ring-trapped Bose-Einstein condensate systems for optimised quantum sensing and/or atomtronic applications.
• Publish findings in high-impact journals and top-tier machine learning conferences.
• Contribute to an open-source codebase to ensure reproducibility and utility for the wider scientific community.
• Collaborate with non-academic partners to translate the research into real-world application.

You will work within a vibrant community of quantum modellers and machine learning academics, centred in MARS. There is additional scope to engage in consultancy, teaching, and outreach activities relevant to the research.

This is a full-time, fixed term position until 31^st July 2029. Flexible working arrangements will be considered but you will be expected to be present on the Lancaster campus a minimum of two days a week.

Candidates who are considering making an application are strongly encouraged to contact Professor Andrew Baggaley [email protected] or Dr Ryan Doran [email protected]

MARS: Mathematics for AI in Real-world Systems is seeking a highly motivated and creative Senior Research Associate to work at the intersection of quantum fluid dynamics and machine learning. You will lead research on the following project

It is an exciting time to be part of MARS, which is based in one of the top-ranked maths departments in the UK. You'll be part of a thriving and collegiate research group with a growing complement of academic staff, researchers and PhD students. MARS is a nationally distinctive group to join if you want to be part of the next generation of mathematicians tackling real-world problems and shaping the future of mathematics and AI.