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Strong employer brand and metro location but highly specialized scientific AI skillset limits applicant pool.
Highly specialized scientific AI, physics, and simulation expertise reduces cross-industry transferability significantly.
Requires top-tier MS/PhD pedigree, niche scientific-ML skills, and research track record, so hiring filters will be strict.
Research and develop AI-assisted engineering methodologies integrating Scientific Machine Learning, physics-based simulation, and numerical optimization for industrial system simulation.
Advance algorithms for interpretable model discovery, extrapolation, hidden-state inference, and multi-physics equation discovery using experimental and simulation data.
Collaborate with international teams to transform engineering simulations and support digital twins, virtual verification, and AI-assisted engineering design in multiple Bosch business sectors.
MS/M.Tech or PhD in Mechanical Engineering, Applied Mathematics, Physics, Computer Science, Control Engineering, Scientific Computing or related disciplines from reputed institutes (IITs, IISc, or top international institutes).
Strong programming skills in Python for scientific computing; experience with NumPy, SciPy, SymPy, Pandas, Matplotlib, and PyTorch or equivalent ML frameworks.
Experience with Scientific Machine Learning methods including Symbolic Regression, Physics-informed Machine Learning, Neural Operators.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in bridging interdisciplinary domains: mathematics, physics, AI, and industrial engineering with a focus on scientific AI technologies for engineering simulation.
Comfortable working independently in international, interdisciplinary research teams with strong analytical and communication skills in English.
Strategically aligns with innovation in AI, numerical optimization, symbolic regression, and emerging technologies such as LLMs to influence future digital engineering workflows.