Researcher AIML enhanced Computational Engineering for Scientific AI and Optimization

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.

Youll 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.

Qualifications :

Educational qualification:

MS/M. Tech PhD from top Indian institutes (IITs IISc etc.) or from top international institutes with good track record of research publications related to Mechanical Engineering Applied Mathematics Physics Computer Science Control Engineering Scientific Computing or related disciplines.

Experience :

Mandatory/requires Skills :

• Strong programming skills in Python for scientific computing and software development.

• Solid understanding of numerical methods optimization and dynamical systems.

• Worked on with Scientific Machine Learning: Symbolic Regression Physics-informed Machine Learning Neural Operators

• Experience with scientific computing libraries (NumPy SciPy SymPy Pandas Matplotlib).

• Experience with PyTorch or equivalent machine learning frameworks.

• Git HPC cloud computing for scientific workloads

• Strong analytical and mathematical problem-solving skills.

• Excellent communication skills in English.

• Ability to work independently in interdisciplinary international research teams.

Preferred Skills :

• Nonlinear optimization Bayesian optimization Evolutionary algorithms

• Familiar with Large Language Models and AI Agents

• Nice to have Reinforcement learning experiences

Personal Competencies

• Passion for scientific innovation and engineering excellence.

• Strong curiosity toward emerging AI/LLM technologies.

• Ability to bridge mathematics physics AI and industrial engineering.

• Excellent critical thinking and structured problem-solving skills.

• Self-driven proactive and comfortable working with open research questions.

• Strong collaboration and communication skills in international environments.

• Willingness to challenge conventional engineering methodologies and contribute to future engineering paradigms.

Why Join Us

You will help build one of the companys strategic technology capabilities at the intersection of Artificial Intelligence Physics and Industrial Simulation.

Your work will directly influence:

• Future simulation methodologies

• AI-assisted engineering design

• Virtual product verification

• Cross-domain engineering applications

• Patent generation and scientific publications

• Strategic collaborations with leading universities and research institutes

Remote Work :

No

Employment Type :

Full-time