Research Associate in Physics-Informed Machine Learning for Crowd Dynamics na Edinburgh University Group
Edinburgh University Group · Edinburgh, Reino Unido · Onsite
- Professional
- Escritório em Edinburgh
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 an 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 (opens in a new tab) 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 (opens new browser tab).
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 browsers local time zone.
About Company
The School of Engineering addresses diverse complex challenges across the entire field of engineering, at the micro- and macro/global scales. At the microscopic scale, its research supports the design and deployment of novel nanotechnology and devices for biological monitoring and medical diagnosis; at the macro/global scale, it optimises the security and sustainability of the built environment and develops engineering solutions to climate change issues. In REF 2021 the joint submission from the University of Edinburgh and Heriot-Watt University to General Engineering was ranked 1st in Scotland and 3rd in the UK for quality and breadth of research. The ranking cited is based upon the breadth and quality of research which apply standard formula (as used by the Times Higher Education) to the REF 2021 results.
The School has a strong track record in producing more than 50 technology spin-outs and developing industry links that enable our graduates to build career-long relationships. Current research income in the School was c.£22.0M in 2020/21.
