Postdoctoral Research Fellow

Mahindra University || Centre for Life Sciences; Hyderabad; On-site; INR 670,000 - 949,000; Full time

Job summary

Mahindra University || Centre for Life Sciences invites applications for a Postdoctoral Research Fellow to work on ANRF (PMECRG) funded project developing high-fidelity computational and machine-learning frameworks for double-diffusive and salt-finger convection.

The role blends fluid mechanics, numerical simulation, HPC, and data-driven modeling, offering collaboration across disciplines and access to advanced computing resources.

Qualifications

• Ph.D. in Aerospace Engineering, Mechanical Engineering, or Applied Mathematics.
• Strong CFD background and numerical methods expertise.
• Experience with CFD modelling of fluid flows.

Responsibilities

• Develop high-fidelity simulations for salt-finger and double-diffusive convection.
• Optimize GPU-accelerated computational frameworks.
• Run DNS/large-scale simulations and analyze results.
• Develop ML-based reduced-order models and integrate with simulations.
• Collaborate across fluid mechanics, HPC, and ML teams.

Skills

CFD expertise

Numerical methods

Python

Fortran

Education

Ph.D. in Aerospace Engineering or Mechanical Engineering or Applied Mathematics

Tools

MPI/OpenMP

GPU computing

Linux HPC

Job description

We are seeking a highly motivated Postdoctoral Research Fellow to work on ANRF (PMECRG) funded project on the development of high-fidelity computational and machine-learning frameworks for double-diffusive and salt-finger convection.

The research will combine: Geophysical flows (Double-diffusive convection: Salt-finger convection, Transport phenomena), High-Fidelity Computing, and Reduced-order modeling.

The position offers an opportunity to work at the intersection of fluid mechanics, numerical simulation, HPC, and machine learning.

Key Responsibilities

• Development of high-fidelity numerical simulations of salt-finger and double-diffusive convection
• Development and optimization of GPU-accelerated computational frameworks
• High-performance DNS and large-scale numerical simulations
• Development of machine-learning-based reduced-order models
• Analysis of complex multiscale flow and transport phenomena
• Integration of simulation data with ML/ROM frameworks
• Scientific software development, automation, and large-scale data analysis

Eligibility Criteria

Essential

• Ph.D. in Aerospace Engineering/Mechanical Engineering/Applied Mathematics, or a closely related discipline
• Strong background in computational fluid dynamics and numerical methods
• Demonstrated research experience in computational modelling of fluid flows

Desirable

• At least 1 year of post-Ph.D. research experience in CFD
• Experience with DNS or LES of complex flows
• Strong understanding of turbulence and numerical methods
• Proficiency in Modern Fortran and Python
• Experience with Linux/Unix-based HPC environments
• Exposure to parallel computing using MPI, OpenMP, and/or GPU computing
• Experience with machine learning, reduced-order modelling, or data-driven methods
• Ability to work independently as well as collaboratively in a multidisciplinary research environment