Researcher Multiphysics AI

Overview

Researcher (Multiphysics AI) focusing on developing and deploying physics-guided Scientific Machine Learning solutions for complex industrial Multiphysics problems.

What you'll do

• Design and develop Scientific ML and physics-guided AI methods for Multiphysics and engineering applications.
• Own and guide decisions on when to apply PINNs, neural operators, surrogates, classical ML, or simulation-centric approaches.
• Work with asset/LOB domain experts to understand asset behavior, operating constraints, uncertainties, and failure modes.
• Translate engineering challenges into well-posed Scientific ML problems and validate models against experimental, simulated, and operational data.
• Check robustness to sparse, biased, or imperfect measurements and balance accuracy, physical consistency, interpretability, and compute efficiency.
• Drive AI-accelerated simulation research including reduced-order modeling and emulation, plus performance optimization for large-scale/time-critical applications.

What you'll need

• PhD (or equivalent industry experience) in Applied Mathematics, Computational Physics, Computational Engineering, or AI/Machine Learning.
• Deep hands-on experience with Scientific ML techniques such as physics-informed and hybrid learning, neural operators, surrogate modelling, or graph-based ML.
• Knowledge of uncertainty quantification, Bayesian inference, or probabilistic ML.
• Strong programming and prototyping skills (e.g., Python and modern ML frameworks).
• Background in at least one major Multiphysics/engineering domain (e.g., CFD, structural/thermal analysis, electrochemistry, materials modelling/discovery).
• Demonstrated experience applying AI/Scientific ML to real complex systems (not only synthetic benchmarks).
• Proven ability to work effectively with domain experts; strong communication across diverse audiences.

Details

• Location: Shell Technology Centre - Bangalore, India.
• Posting start date: September 1, 2026.
• Employment type: Regular.