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Tier-1 employer and Bangalore metro increase applicant density despite niche senior modeling specialization.
Domain-specific semiconductor modeling requirements are transferable from aerospace/turbine fields but remain moderately specialized.
Strict seniority, domain-specific CFD/heat-transfer and ML modeling requirements make shortlisting highly selective.
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Develop physics-based models for Thermal/CFD/Chemistry applications in semiconductor capital equipment, using tools like ANSYS Fluent, Star CCM+, or COMSOL.
Utilize Design of Experiments (DOE), optimization, statistical methods, and AI/ML techniques to correlate simulation data with experimental data and improve product designs.
Collaborate cross-functionally with mechanical, electrical, process, and software engineers to define design requirements and provide quantitative design improvements based on field data.
Master's degree in Mechanical/Chemical Engineering with 10+ years experience or PhD with 5+ years experience in Computational Fluid Dynamics, Heat Transfer, or related fields such as semiconductor, aerospace, automotive, or equivalent.
Proficiency with AI/ML concepts and hybrid physics-based AI/ML modeling software, including coding skills in machine learning frameworks like TensorFlow, PyTorch, or Scikit Learn.
Strong knowledge in fluid mechanics, heat transfer fundamentals, closed-form solution development, and statistical methods including uncertainty quantification and Bayesian optimization.
Work Experience Required: Minimum 10+ years (Master's) or 5+ years (PhD) as specified; Notice period: Not explicitly mentioned in the JD.
Experienced in driving product development and owning design decisions with strong cross-functional collaboration skills in multidisciplinary teams.
Demonstrates strong critical thinking and analytical skills leveraging first principles thinking, statistical analysis, and physics-based insights.
Capable of translating complex simulation and experimental data into actionable design improvements using advanced AI/ML and statistical techniques within a dynamic R&D environment.