Postdoctoral Research Associate

UE07: £41,064 - £48,822 per annum CSE / School of Mathematics Full time: 35 hours/week Fixed-term: 36 months The Opportunity: Solutions of long and flexible polymer molecules do not flow like water. Even when flowing slowly, these fluids exhibit hydrodynamic instabilities and a new type of turbulence, so-called elastic turbulence (ET). When flowing faster, polymer solutions exhibit a new state – elasto-inertial turbulence (EIT) – seen in subcritical wall-bounded (pipe) flows. Considerable progress has been made in probing ET and EIT flows in state-of-the-art viscoelastic models, but questions remain as to how these models actually connect to experimental observations. At the heart of this is the continuing suspicion about the fidelity of theoretical models to describe experiments. An opportunity thus exists to develop new methods for the investigation of viscoelastic fluid flows that rely on integrating experimental, theoretical, and machine-learning methods that can lead to quantitative agreement between experimental observations and models. This postdoc forms parts of an integrated research program in which experiments and simulations will be designed to access extensive time and space information in order to perform experiment/simulation comparison. We will use statistical methods to identify similarities and discrepancies between experiments and simulations, as well as machine learning tools. The project itself falls into three separate “aims”, and the post holder will work on all three: Aim 1 will focus on 2D flows and simulations to obtain the best possible agreement (statistics, flow structures, etc) between experiments and simulations. ML methods will be developed and applied to identify and optimize simulation parameters. An important part of this aim is to estimate the polymer stresses in the experimental flows using data-driven methods. Aim 2 will explore the uniqueness of the elastic turbulence flow state in flows with streamline curvature and in parallel shear flows, in both 2D and 3D flows. Aim 3 will relax the low inertia condition and explore the relationship between ET and EIT in 2D flows. There will be extensive opportunities for travel to visit our experimental collaborators at the University of Pennsylvania, as well as collaborators in DAMTP, University of Cambridge and the University of Vermont. This post is full-time (35 hours per week). View the full job description (opens in a new browser tab) How to apply Please include the following documents in your application: CV Research statement, including details as to how this project helps your career trajectory The names of two academic references References will be sought from shortlisted candidates 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, including generous annual leave entitlement, a defined benefits pension scheme, a wide range of staff discounts, family-friendly initiatives, and flexible work options. Check out the full list on our staff benefits page (opens in a new tab) and use our reward calculator to discover the 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 a Stonewall Proud Employer, actively promoting LGBTQ+ equality. We welcome applications from all qualified candidates and particularly encourage applications from women and other under-represented groups in applied mathematics. 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 17 September 2026. Unless stated otherwise the closing time for applications is 11:59pm UK time. If you are applying outside the UK the closing time on our adverts automatically adjusts to your browsers local time zone. Interviews will be held in early October.

• This post is full-time (35 hours per week).
• View the full job description (opens in a new browser tab)
• Solutions of long and flexible polymer molecules do not flow like water.
• Even when flowing slowly, these fluids exhibit hydrodynamic instabilities and a new type of turbulence, so-called elastic turbulence (ET).
• When flowing faster, polymer solutions exhibit a new state – elasto-inertial turbulence (EIT) – seen in subcritical wall-bounded (pipe) flows.
• Considerable progress has been made in probing ET and EIT flows in state-of-the-art viscoelastic models, but questions remain as to how these models actually connect to experimental observations.
• At the heart of this is the continuing suspicion about the fidelity of theoretical models to describe experiments.
• An opportunity thus exists to develop new methods for the investigation of viscoelastic fluid flows that rely on integrating experimental, theoretical, and machine-learning methods that can lead to quantitative agreement between experimental observations and models.
• This postdoc forms parts of an integrated research program in which experiments and simulations will be designed to access extensive time and space information in order to perform experiment/simulation comparison.
• We will use statistical methods to identify similarities and discrepancies between experiments and simulations, as well as machine learning tools.
• The project itself falls into three separate “aims”, and the post holder will work on all three: Aim 1 will focus on 2D flows and simulations to obtain the best possible agreement (statistics, flow structures, etc) between experiments and simulations.
• ML methods will be developed and applied to identify and optimize simulation parameters.
• An important part of this aim is to estimate the polymer stresses in the experimental flows using data-driven methods.
• Aim 2 will explore the uniqueness of the elastic turbulence flow state in flows with streamline curvature and in parallel shear flows, in both 2D and 3D flows.
• Aim 3 will relax the low inertia condition and explore the relationship between ET and EIT in 2D flows.
• There will be extensive opportunities for travel to visit our experimental collaborators at the University of Pennsylvania, as well as collaborators in DAMTP, University of Cambridge and the University of Vermont.
• job description (opens in a new browser tab)