Bosch Group: Researcher: AI/ML enhanced Computational Engineering for Scientific AI and Optimization — Bengaluru, India

About Bosch Group

Bosch's India activities span mobility, industrial technology, consumer goods, and energy and building technology. Its employer description identifies engineering development, manufacturing and commercial operations within that range. The group operates through multiple Indian businesses, including activities in automotive systems, software and engineering, industrial technology and home appliances. This makes Bosch a collection of related technology businesses rather than a single product operation, with different entities serving different industrial and consumer applications.

The position and its setting

Bosch Group lists Researcher: AI/ML enhanced Computational Engineering for Scientific AI and Optimization in Bengaluru, India, under reference REF295166F. The details below separate the work described by the employer from essential conditions, preferred experience and practical preparation advice.

• Employment type: Full-time
• Employer listing date: 1 September 2026
• Contract type: Unlimited
• Legal Entity: Bosch Global Software Technologies Private Limited
• Brands: Bosch Group

Responsibilities and work described

• 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
• 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
• Development: 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

Essential requirements and candidate background

• MS/M
• Experience: 3 + years of experience
• Mandatory/required Skills:
• Programming skills in Python for scientific computing and software development, is non-negotiable
• Understanding: 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
• 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: scientific computing libraries (NumPy, SciPy, SymPy, Pandas, Matplotlib)
• Experience: PyTorch or equivalent machine learning frameworks
• Git, HPC, cloud computing for scientific workloads
• Analytical and mathematical problem-solving skills
• Knowledge: ODEs and PDEs and linear algebra
• Communication skills in English
• Work independently in interdisciplinary international research teams

Preferred experience and additional strengths

• 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
• Curiosity toward emerging AI/LLM technologies
• Bridge mathematics, physics, AI, and industrial engineering
• Critical thinking and structured problem-solving skills
• Self-driven, proactive, and comfortable working with open research questions
• Collaboration and communication skills in international environments
• Willingness to challenge conventional engineering methodologies and contribute to future engineering paradigms
• Why Join Us?

Location, schedule and contract conditions

• Work arrangement: On-site
• Advertised location: Bengaluru, India

Conditions requiring the complete notice

Some passages contain complex or combined conditions that have not been reduced to short eligibility statements here. Read the complete linked notice for those passages, including any alternatives, exceptions or restrictions. The concise facts above should not be treated as the full wording of every condition.

Pay and application dates

A verified salary figure is not established in this article. An online estimate for another Researcher: AI/ML enhanced Computational Engineering for Scientific AI and Optimization vacancy is not an offer for reference REF295166F; use the employer’s own pay disclosure and recruitment communication.

A closing date is not confirmed in this article. Check the current employer page before submitting: an undated posting can change or be withdrawn.

How to apply to Bosch Group

Applications follow the official employer page linked below.

Open the official Bosch Group posting below and match the title and reference REF295166F.

Compare your qualifications and experience with the essential conditions for the Bengaluru, India position, then read the preferred criteria separately.

Prepare an accurate CV and the supporting items requested in that employer’s application. Provide only information and documents you are authorised to share.

Use the employer’s application option, complete the required fields and review the submission. Retain the acknowledgement when one is provided. Job Alerts World does not receive the application.

Practical preparation: Python

Python work is easier to assess through a concrete example than a list of libraries. Explain the problem, the input data, the parts you implemented and how you checked the output. Include a repository or a short technical note when you can share it.

Relevant skills

• Python
• AI/ML
• LLMs
• COMSOL
• machine learning
• AI
• Git
• mechanical engineering
• communication