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Tier-1 brand, metro location, and mid-level AI research title increase applicant competition despite niche skills.
Deep computational mechanics, PDEs, and scientific ML requirements limit transferability across industries.
Explicit experience, mandatory numerical-methods expertise, and ML research skills impose strict shortlisting filters.
Develop and advance Scientific ML technologies combining physics-based simulation, symbolic regression, numerical optimization, and LLMs to enhance industrial system simulation and engineering workflows.
Research and create novel methods for discovering governing equations from experimental and simulation data under partial observability and uncertain boundary conditions.
Benchmark AI technologies and collaborate internationally with experts to support future digital twins, virtual verification, and AI-assisted engineering design across multiple Bosch sectors.
Master's or PhD from top Indian (IITs, IISc) or international institutes with research in Mechanical Engineering, Applied Mathematics, Physics, Computer Science, Control Engineering, Scientific Computing, or related fields.
Minimum 3 years of professional experience.
Strong programming skills in Python for scientific computing with experience in scientific computing libraries (NumPy, SciPy, SymPy, etc.) and ML frameworks like PyTorch.
Solid theoretical knowledge of computational mechanics and numerical methods (e.g., Finite Element, Finite Volume, Finite Difference) beyond application use, plus understanding of ODEs, PDEs, and linear algebra.
Experienced researcher combining physics, AI, and mathematics with practical skills in Scientific Machine Learning and computational mechanics aiming to innovate engineering simulation.
Comfortable working independently and collaboratively in interdisciplinary, international research teams focused on AI-assisted scientific discovery and engineering design.
Ability to bridge advanced numerical techniques with ML methodologies and challenge traditional engineering paradigms to influence future simulation technologies.