Openings

Project Overview
Large Eddy Simulation (LES) of turbulent flows require modelling the influence of unresolved (subgrid) scales on the resolved flow variables. In practice, most existing subgrid-scale (SGS) closures approximate this influence using formulations that are local in time. However, theoretical developments based on Koopman operator theory and the Mori–Zwanzig (MZ) formalism rigorously show that the effect of unresolved variables on resolved dynamics is inherently nonlocal in time, introducing memory effects. This highlights a fundamental gap between current LES closures and the underlying dynamical systems theory. This project will develop memory-aware deep-learning SGS models by combining optimal prediction for MZ technique with autoencoder-based neural networks. The goal is to construct practical modifications of widely used LES models that incorporate learned memory effects, thereby improving predictive accuracy and physical consistency in turbulence simulations significantly.

Qualifications

• Ideally hold a Master’s degree with Merit or Distinction (or equivalent) in Aerospace/Mechanical Engineering, Applied Mathematics, Physics, Computer Science, or a related field.
• Hold (or expect to achieve) a First Class or 2:1 UK honours degree (or international equivalent to be checked with UoM admissions).
• Background in one or more of the following: fluid mechanics, applied mathematics and/or dynamical systems, probability/statistics, scientific computing.
• Familiarity with machine learning is a plus.
• Demonstrable excellent communication skills (see IELTS requirements on link given below)

Why Join Us
– Joint supervision from two internationally active research groups
– Training at the intersection of dynamical systems, AI/ML, and fluid mechanics
– Opportunity to work across India and the UK
– Access to high-performance computing facilities and collaborative research networks
– Exposure to cutting-edge research in Koopman/MZ theory and turbulence modelling

This programme is ideal for candidates aiming to build a research career in advanced CFD, turbulence modelling, applied mathematics and machine learning.

How to Apply
See application process on the website link given below.

Before submitting the formal application, interested candidates should email Dr. Rishita Das at rishitadas@iisc.ac.in their CV using the subject line “Expression of Interest – Joint PhD IISc–UoM”. Shortlisted candidates will then be guided through the formal joint application process via the official portal. We look forward to receiving your email!

Qualifications

• PhD degree in Aerospace/Mechanical Engineering, Applied Mathematics, Physics, Computer Science, or a related field.
• Fundamental knowledge in probability and statistics.
• Background in fluid mechanics and/or turbulence.
• Proficiency in Python or a comparable language used for data analysis and modeling.
• Familiarity with machine learning is a plus.
• Excellent problem-solving skills, critical thinking, and a passion for research.

Why Join Us
This position offers a unique chance to gain hands-on experience in cutting-edge data-driven methods for discovering the complexities of turbulent flow, with access to significant data-science/computational resources. As part of a funded project, you will have the opportunity to build a robust research portfolio. Successful performance in this pre-PhD role may serve as a stepping stone to our PhD program in one or more years of joining.

How to Apply
Please submit a single PDF containing:

1. A cover letter explaining your motivation and relevant background.
2. A detailed CV highlighting educational qualifications, research projects, programming experience, and future research interests.
3. Contact information for at least two references.

Send applications or inquiries to rishitadas@iisc.ac.in, using the subject line “Application for Project Associate”. Review of applications will begin immediately and continue until the position is filled. We look forward to receiving your application!

Interested graduate students can email Prof. Rishita Das at rishitadas@iisc.ac.in or stop by her office (AE-210).

Candidates who have not applied to IISc yet, can apply through IISc admissions conducted twice a year. We look forward to receiving your application!

Interested candidates can email their CV mentioning their interests/experience to Prof. Rishita Das (rishitadas@iisc.ac.in).
Candidates are encouraged to apply for fellowship programs such as JNCASR, Indian Academy of Sciences.