Researcher: AI/ML enhanced Computational Engineering for Scientific AI and Optimization
Robert Bosch GmbHMatch Score
Against your primary resumeLogin to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Protocol Intelligence
Data-driven signals on your job's competitivenessTier-1 brand and metro location increase applicant density, but niche scientific ML reduces generalist competition.
Requires specialized computational mechanics, PDE knowledge, and scientific ML, limiting cross-industry transferability.
Explicit 3+ years plus deep numerical, scientific ML, and Python/PyTorch requirements enforce strict shortlisting.
Job Description
Structured overview of role & requirementsAbout This Role
Develop and advance AI-enhanced scientific machine learning methodologies for interpretable physical model discovery in industrial system simulation.
Collaborate with international interdisciplinary teams to integrate physics-based simulation, symbolic regression, numerical optimization, and LLMs for next-generation digital engineering workflows.
Benchmark and define technical directions for future AI technology development in virtual product development and AI-assisted engineering design related to multiple Bosch business sectors.
Minimum Requirements
3+ years of relevant work experience in scientific machine learning, computational mechanics, or related fields.
MS/M.Tech or PhD from top Indian or international institutes in Mechanical Engineering, Applied Mathematics, Physics, Computer Science, Control Engineering, Scientific Computing, or related disciplines.
Strong programming skills in Python focused on scientific computing and software development.
Solid understanding and hands-on experience with computational mechanics, numerical methods (Finite element, Finite Volume, Finite difference), and related theory (not just tool usage).
Ideal Candidate Profile
Experienced with advanced scientific ML methods including symbolic/sparse regression, physics-informed ML, operator learning, and system identification.
Capable of working independently in international interdisciplinary research teams integrating AI, physics, and industrial engineering.
Knowledgeable in scientific computing libraries (NumPy, SciPy, PyTorch), numerical optimization, and familiar with emerging AI technologies such as LLMs and AI agents.