Project Associate at the Dept. of Aerospace, IISc, Bangalore – Physics 
informed machine learning methods for predicting intermittent events in 
fluid flows

About the job: Extreme events in fluid flows are a fascinating and crucial 
area of research. Predicting and perhaps mitigating against such events is 
important for various applications. Recent studies have shown that classical 
reduced order modelling techniques like proper orthogonal decomposition 
(POD) can identify coherent patterns in fluid flows that are linked to such 
extreme events. These coherent patterns can be used to predict such events. 
This project takes this idea further by exploring the use of physics 
informed machine learning (ML) techniques to identify flow patterns and  
therefore predict extreme events. Importantly, the question is how the ML 
techniques compare to the classical POD-based methods. 

Qualification: Bachelors or Masters in Physics, Mechanical Engineering, 
Aerospace Engineering or related disciplines. Experience with programming 
languages like MATLAB or Python is required. An understanding of fundamental 
machine learning techniques will be relevant to the project. 

Duration: 6 months (including a 1-month probationary period) starting 
immediately. Possible extensions up to 1 year.

Salary: As per the standard IISc project associate salary scales.

Deadlines: For full consideration please send CV and a cover letter to the 
email anagham@iisc.ac.in before the end of 18th of December, 2024. However, 
the position will be open until filled. In you email, please include the 
subject line ‘PA: PIML methods for intermittent flows’. 

Contact: Dr. Anagha Madhusudanan (DST-INSPIRE Faculty Fellow, Dept. of 
Aerospace Engg.). Email: anagham@iisc.ac.in.