Researcher: AI/ML enhanced Computational Engineering for Scientific AI and Optimization

Company Description

Bosch Global Software Technologies Private Limited is a 100% owned subsidiary of Robert Bosch GmbH, one of the world's leading global supplier of technology and services, offering end-to-end Engineering, IT and Business Solutions. With over 27,000+ associates, it’s the largest software development center of Bosch, outside Germany, indicating that it is the Technology Powerhouse of Bosch in India with a global footprint and presence in the US, Europe and the Asia Pacific region.

Job Description

The aim is to develop next-generation Scientific AI technologies that enable automatic discovery, refinement, and deployment of interpretable physical models for industrial system simulation. The role will contribute to the long-term development of AI-assisted engineering methodologies, bridging physics-based simulation, symbolic regression, numerical optimization, and Large Language Models (LLMs) for future digital engineering workflows.

You’ll be a part of an innovation team dedicated to transforming engineering simulation through Artificial Intelligence. Our mission is to accelerate virtual product development by combining scientific machine learning, physical modeling, and industrial simulation technologies. Working closely with simulation experts, applied mathematicians, AI researchers, and product development teams across Germany, Spain, India, USA, and China, we develop methodologies that support future digital twins, virtual verification, and AI-assisted engineering design across multiple Bosch business sectors, including Home Appliances, HVAC, Mobility, and Power Tools.

Your responsibilities will involve:

- Research and evaluate state-of-the-art methodologies in Scientific Machine Learning, Symbolic Regression, AI-assisted scientific discovery, and physics-informed AI.

- Develop novel methodologies for discovering governing equations from experimental and simulation data under partial observability and uncertain boundary conditions.

- Advance algorithms for interpretable model discovery, extrapolation, hidden-state inference, and multi-physics equation discovery.

- Nice to have: LLM reasoning, numerical optimization, evolutionary search, and domain knowledge.

- Nice to have: Publish technical papers, patents. Benchmark emerging AI technologies and define technical direction for future feature development.

Educational qualification: