Researcher: AI/ML enhanced Computational Engineering for Scientific AI and Optimization
Company: Bosch Group
Role summary
Researcher: AI/ML enhanced Computational Engineering for Scientific AI and Optimization is a role at Bosch Group in Bengaluru, Karnataka, India (on-site). Posted 2026-09-01. Experience level: 2-5.
- Job title
- Researcher: AI/ML enhanced Computational Engineering for Scientific AI and Optimization
- Company
- Bosch Group
- Location
- Bengaluru, Karnataka, India
- Work arrangement
- on-site
- Category
- Software Engineering
- Experience level
- 2-5
- Date posted
- 2026-09-01
Bosch is looking for a Researcher to help them build scientific AI and optimization tools using AI/ML in computational engineering. This is a fantastic opportunity to work with cutting-edge AI technology and make a real impact on future innovations.
Job description
Company Description
At Bosch Research , research meets application. Around 1,700 experts around the world develop the technologies of tomorrow and beyond. The world in which we live is changing rapidly. Global megatrends such as climate change and digitalization influence virtually every area of day-to-day life. One thing that doesn’t change is Bosch’s mission to create technology that is “Invented for life” in other words using technological innovations to improve people’s quality of life and using natural resources responsibly. Bosch Research sees itself as the spearhead of innovation at Bosch: Our researchers are involved in around a quarter of the patent applications filed by Bosch. On the one hand, we help all Bosch divisions to develop the next generation of products or the generation after that. On the other hand, we look far into the future: with our partners in science and industry, we identify technologies and trends which will shape our lives in the future and make them usable for the entire company. As a global team with around 1,700 experts from all over the world, we cover a wide range of areas with our research. Whether it be automation, digitalization and connectivity, artificial intelligence (AI), generative artificial intelligence (Gen AI), electrification, climate action and sustainability or healthcare: Bosch Research combines top-level research with specific technological developments.
For more information visit: https://www.bosch.com/research/
The Research and Technology Center at Bangalore focuses on research in real-time connected mobility, computational engineering, numerical method, hybrid modelling (system modelling using a combination of physics and AI/ML), time series analysis, natural language processing, computer vision, Gen AI applications and embedded real time systems. We pursue research and development by working with top-tier universities worldwide and our corporate partners.
Job Description
The aim is to develop next-generation Scientific ML 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.
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 : 3 + years of experience
Mandatory/required Skills :
Strong programming skills in Python for scientific computing and software development, is non-negotiable.
Solid understanding of computational mechanics/ numerical methods, optimization, and dynamical systems.
Profound knowledge and experience of advanced numerical techniques like Finite element, Finite Volume and Finite difference methods etc. If you know only to use numerical methods via simulation tools like ANSYS, ABAQUs, COMSOL, OpenFOAM etc. and don’t have in depth knowledge of the theory behind the methods this is not a right role for you. You should know very well how the traditional numerical methods work as the tasks will be to complement and improve on that using ML methods and not just about using ML methods for problems. So basics on computational mechanics/numerical methods is a must.
Worked on with Scientific Machine Learning: Symbolic and Sparse Regression, Physics-informed Machine Learning, Operator learning, system identification
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.
Knowledge of ODEs and PDEs and linear algebra.
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 exce