Research Associate in Physics-Informed Machine Learning for Crowd Dynamics

The University of Edinburgh; City of Edinburgh; On-site; GBP 41,000 - 49,000; Full time

Job summary

A leading academic institution in Edinburgh is seeking a Postdoctoral Research Associate. This role focuses on developing a physics-informed machine learning pipeline for crowd dynamics simulation, impacting public safety and urban planning. Applicants should have a PhD or be near completion in a relevant field, along with expertise in machine learning and scientific programming. A competitive salary and a vibrant working environment are offered.

Benefits

Competitive salary

Generous holiday entitlement

Comprehensive staff benefits

Diverse international community

Qualifications

• PhD (or be near completion) in a relevant field.
• Strong expertise in machine learning and scientific computing.
• Solid understanding of mathematical modelling of physical systems.
• Proficiency in scientific programming.

Responsibilities

• Develop physics-informed machine learning pipeline for crowd models.
• Collaborate with a PhD student on physics-based models.
• Create a real-time, open-source simulator for crowd dynamics.

Skills

Machine learning

Scientific computing

Mathematical modelling

Scientific programming (Python, Fortran, C++)

Analytical skills

Problem-solving skills

Communication skills

Education

PhD in Engineering, Physics, Applied Mathematics, Computer Science, or related field

Job description

Grade UE07 £41,064 - £48,822 per annum

College of Science & Engineering / School of Engineering / Institute for Multiscale Thermofluids / Machine Learning, Computational Engineering, Crowd Dynamics

Full-time: 35 hours per week

Fixed Term: from 1st March 2026, for up to 36 months

We are looking for a talented, creative, and experienced Postdoctoral Research Associate to join the EPSRC-funded project FLOCKS (Fluid dynamics-Like Open-source Crowd Knowledge-driven Simulator).

The Opportunity:

Designed in close collaboration with industry leaders, FLOCKS aims to create the world's first real-time, open-source simulator of large, dense crowd dynamics. The simulator will have applications in public safety, urban planning and event management. The research will focus on developing a physics-informed machine learning pipeline to derive governing equations and boundary conditions for macroscopic crowd models from synthetic and real-world data. Close collaboration with a dedicated PhD student, who is developing physics-based models and generating synthetic datasets, will fuel the machine learning framework while also offering a valuable opportunity for mentorship. Thanks to its partnerships with world-leading experts in crowd safety engineering and open-source software development, the project will have a direct impact on real-world applications relating to public safety, urban planning and event management. A final demonstrator will simulate iconic local events (e.g. Hogmanay on Princes Street, an Edinburgh derby football match, or a Murrayfield Stadium concert) using pre-captured datasets to demonstrate the simulator's predictive power and direct relevance to these applications. This is an excellent opportunity for an experienced researcher interested in machine learning, mathematical modelling, and complex systems.

Your skills and attributes for success:

• PhD (or be near completion) in Engineering, Physics, Applied Mathematics, Computer Science, or a related field.
• Strong expertise in machine learning and scientific computing.
• Solid understanding of the mathematical modelling of physical systems.
• Proficiency in scientific programming (e.g., Python, Fortran, C++).
• Strong analytical, problem-solving, and communication skills.

Click to view a copy of the full job description

As a valued member of our team you can expect:

• A competitive salary
• An exciting, positive, creative, challenging and rewarding place to work.
• To be part of a diverse and vibrant international community
• Comprehensive Staff Benefits, such as a generous holiday entitlement, competitive pension schemes, staff discounts, and family-friendly initiatives. Check out the full list on our staff benefits page and use our reward calculator to discover the total value of your pay and benefits

Championing equality, diversity and inclusion

The University of Edinburgh holds a Silver Athena SWAN award in recognition of our commitment to advance gender equality in higher education. We are members of the Race Equality Charter and we are also Stonewall Scotland Diversity Champions, actively promoting LGBT equality.

Prior to any employment commencing with the University you will be required to evidence your right to work in the UK. Further information is available on our right to work webpages.

The University may be able to sponsor the employment of international workers in this role. This will depend on a number of factors specific to the successful applicant.

Key dates to note

The closing date for applications is 15 October 2025.

Unless stated otherwise the closing time for applications is 11:59pm GMT. If you are applying outside the UK the closing time on our adverts automatically adjusts to your browser's local time zone.