MARS Senior Research Associate in Machine Learning to Improve Sensing in Quantum Gases - 0307-26 in Burnley
MARS Senior Research Associate in Machine Learning to Improve Sensing in Quantum Gases - 0307-26

MARS Senior Research Associate in Machine Learning to Improve Sensing in Quantum Gases - 0307-26 in Burnley

Burnley Full-Time 39906 - 46049 £ / year (est.) Home office (partial)
Lancaster University

At a Glance

  • Tasks: Develop machine learning methods for quantum sensing and atomtronic applications.
  • Company: Join a top-ranked maths department at Lancaster University.
  • Benefits: Competitive salary, flexible working, and opportunities for consultancy and outreach.
  • Other info: Collaborative environment with a focus on diversity and inclusion.
  • Why this job: Be part of groundbreaking research in quantum technologies and AI.
  • Qualifications: Experience in machine learning and a passion for quantum fluid dynamics.

The predicted salary is between 39906 - 46049 £ per year.

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. This project will investigate how machine learning can be used to design, control, and interpret ultracold-atom devices in ring-trapped Bose–Einstein condensates (BECs). Ring traps support persistent currents, vortices, and coherent matter-wave dynamics, making them promising platforms for quantum sensing and atomtronics. We will combine modern data-driven approaches emerging in the machine learning literature with established physical models to optimise trap parameters, control protocols, and readout strategies for acceleration and rotational sensors. The project will sit at the intersection of quantum fluid dynamics and machine learning to help build robust, high-performance quantum technologies.

Key Responsibilities

  • 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 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.

Why join MARS

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.

Lancaster University promotes equality of opportunity and diversity within the workplace. For these positions, we welcome applications from all diversity groups but particularly from women who are currently underrepresented in the mathematical sciences. The University recognises and celebrates good employment practice undertaken to address all inequality in higher education whilst promoting the importance and wellbeing for all our colleagues. We warmly welcome applicants from all sections of the community regardless of their age, religion, gender identity or expression, race, disability or sexual orientation, and are committed to promoting diversity, and equality of opportunity.

MARS Senior Research Associate in Machine Learning to Improve Sensing in Quantum Gases - 0307-26 in Burnley employer: Lancaster University

Joining the MARS team at Lancaster University offers an exceptional opportunity to work at the forefront of quantum fluid dynamics and machine learning within a supportive and collaborative environment. With a commitment to diversity and equality, the university fosters a vibrant culture that encourages innovation and professional growth, while providing access to cutting-edge resources and a network of leading academics. This role not only allows you to contribute to groundbreaking research but also to engage in consultancy and outreach activities, making it a truly rewarding career choice.
Lancaster University

Contact Detail:

Lancaster University Recruiting Team

StudySmarter Expert Advice 🤫

We think this is how you could land MARS Senior Research Associate in Machine Learning to Improve Sensing in Quantum Gases - 0307-26 in Burnley

✨Tip Number 1

Network like a pro! Reach out to current employees at MARS or similar organisations on LinkedIn. A friendly chat can give us insights into the company culture and maybe even a referral!

✨Tip Number 2

Prepare for the interview by diving deep into quantum fluid dynamics and machine learning. We should be ready to discuss how our skills align with their projects, especially around ultracold-atom devices.

✨Tip Number 3

Showcase our passion for research! During interviews, let’s share our previous projects and how they relate to optimising quantum sensing. Real-world applications are key, so let’s highlight those!

✨Tip Number 4

Don’t forget to apply through our website! It’s the best way to ensure our application gets noticed. Plus, we can tailor our submission to fit exactly what MARS is looking for.

We think you need these skills to ace MARS Senior Research Associate in Machine Learning to Improve Sensing in Quantum Gases - 0307-26 in Burnley

Machine Learning
Quantum Fluid Dynamics
Data-Driven Approaches
Bose-Einstein Condensates (BECs)
Optimisation Techniques
Control Protocols
Readout Strategies
High-Impact Publishing
Open-Source Development
Collaboration with Non-Academic Partners
Research Translation
Consultancy Skills
Teaching Skills
Outreach Activities

Some tips for your application 🫡

Tailor Your Application: Make sure to customise your CV and cover letter for the MARS Senior Research Associate role. Highlight your experience in machine learning and quantum fluid dynamics, and show us how your skills align with the project goals.

Show Your Passion: We love seeing candidates who are genuinely excited about the intersection of machine learning and quantum technologies. Share your enthusiasm in your application and let us know why this project speaks to you!

Be Clear and Concise: When writing your application, keep it straightforward. Use clear language and avoid jargon unless necessary. We want to understand your qualifications and ideas without getting lost in complex terminology.

Apply Through Our Website: Don’t forget to submit your application through our official website! It’s the best way to ensure we receive all your materials and can consider you for this exciting opportunity.

How to prepare for a job interview at Lancaster University

✨Know Your Stuff

Make sure you brush up on the latest developments in machine learning and quantum fluid dynamics. Familiarise yourself with key concepts related to Bose-Einstein condensates and how they relate to your potential role. This will not only show your enthusiasm but also your expertise.

✨Showcase Your Projects

Prepare to discuss any relevant projects you've worked on, especially those involving data-driven methods or open-source contributions. Be ready to explain your thought process, challenges faced, and how you overcame them. This gives interviewers insight into your problem-solving skills.

✨Ask Smart Questions

Come prepared with thoughtful questions about the MARS project and its goals. Inquire about collaboration opportunities with non-academic partners or how the team approaches translating research into real-world applications. This shows your genuine interest in the role and the organisation.

✨Be Yourself

While it's important to demonstrate your qualifications, don't forget to let your personality shine through. The MARS team values creativity and motivation, so be authentic and share your passion for the intersection of machine learning and quantum technologies.

MARS Senior Research Associate in Machine Learning to Improve Sensing in Quantum Gases - 0307-26 in Burnley
Lancaster University
Location: Burnley

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