13105 - Research Associate in Physics-Informed Machine Learning for Crowd Dynamics
| Posting date: | 17 September 2025 |
|---|---|
| Salary: | £41,064 to £48,822 per year |
| Hours: | Full time |
| Closing date: | 15 October 2025 |
| Location: | Edinburgh, Scotland |
| Remote working: | On-site only |
| Company: | University of Edinburgh |
| Job type: | Contract |
| Job reference: | 13105 |
Summary
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 dates: 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.
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 dates: 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.