Machine Learning to Improve Sensing in Quantum Gases

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^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 a.baggaley1@lancaster.ac.uk or Dr Ryan Doran r.doran@lancaster.ac.uk    

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.  

Closing Date: 17 May 2026

Department: Research

Salary: £39,906 to £46,049 (Full-Time/Indefinite with End Date)

You will lead research in one/both of the following cutting-edge areas:

• Generative Inference and Monte Carlo Optimisation: Developing new generative machine learning approaches, to improve the efficiency of high-dimensional Monte Carlo algorithms for stochastic epidemic models. Research directions may include discrete normalising flows, diffusion-based methods, online reinforcement learning methods, amortized inference.  The aim is to solve one of the last remaining barriers to successful disease modelling at scale, delivering faster and more reliable inference, better-calibrated predictive uncertainty, and computational tools for large-scale mechanistic models.
• Probabilistic Modelling of Higher-Order Contact Structure: Developing novel machine learning and statistical methodology for latent relational structure in populations, including higher-order, group-based, and temporally evolving interactions. Directions may include probabilistic graph and hypergraph models, generative approaches to large-scale contact networks, learning from partial or aggregate observations, and principled uncertainty quantification. The goal is to build scalable methods for inference and intervention-aware analysis in complex epidemic systems, with applications to targeted intervention design in settings such as schools, workplaces, and hospitality.

Key responsibilities

• Develop and implement novel ML architectures and computationally intensive statistical methodology tailored to outbreak datasets.
• Collaborate with public health stakeholders and data providers to ensure models are grounded in real-world contact patterns.
• Publish findings in high-impact journals (e.g., Nature Communications, Lancet Digital Health) and top-tier ML conferences (NeurIPS, ICML, ICLR).
• Contribute to an open-source codebase to ensure reproducibility and utility for the wider scientific community.

You will work within a vibrant community of infectious disease modellers, centred in MARS, but collaborating with colleagues in Lancaster Medical School.  There is additional scope to work within a wider collaboration with the University of St Andrews and Liverpool School of Tropical Medicine in Global Health, human, animal, and OneHealth epidemiology, as well as engage in consultancy, teaching, and outreach activities relevant to the research.

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

Candidates considering making an application are strongly encouraged to contact Professor Chris Jewell c.jewell@lancaster.ac.uk or Dr Jess Bridgen j.bridgen@lancaster.ac.uk.   

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.  

Closing Date: 17 May 2026

Department: Research

Salary: £39,906 to £46,049 (Full-Time/Indefinite with End Date)

This £4M programme is funded by EPSRC and brings together research groups from the Universities of Lancaster, Bristol, Warwick and the London School of Economics together with a committed group of industrial and public sector partners. 

Interaction between the research groups at the universities will be strongly encouraged and resourced; our philosophy is to tackle the methodological, theoretical and computational aspects of these statistical problems together. This integrated approach is essential to achieving the substantive fundamental advances in statistics envisaged, and to ensuring that our new methods are sufficiently robust and efficient to be widely adopted by academics, industry and society more generally.

The programme is led by Idris Eckley (Lancaster University), Haeran Cho (University of Bristol), Paul Fearnhead (Lancaster University), Qiwei Yao (London School of Economics) and Yi Yu (University of Warwick). 

This 2-year position is available at Lancaster University. You should have, or be close to completing, a PhD in Statistics or a closely related discipline. Throughout, you should have demonstrated an ability to develop new statistical theory and methods in one of the relevant areas, including but not limited to: anomaly detection; changepoint analysis; non-stationary time series analysis, high dimensional statistics, statistical-computational tradeoffs, scalable statistical methods. You will also have shown a demonstrable ability to produce academic writing of the highest publishable quality.  

This is a are full-time position, though we will consider applicants requesting part-time or other flexible working arrangements. 

Candidates who are considering making an application are strongly encouraged to contact Idris Eckley (i.eckley@lancaster.ac.uk) or Paul Fearnhead (p.fearnhead@lancaster.ac.uk) to discuss the programme in greater detail.  

Closing Date: 30 May 2026

Department: Research

Salary: £39,906 to £46,049 (Full-Time/Indefinite with End Date)