Modeling Engineer 5 (Thermal, CFD, AI/ML)

Developing physics-based models for Thermal/CFD/Chemistry applications for components in semiconductor capital equipment industry. Experience in commercial software like ANSYS Fluent, Star CCM+, or COMSOL, etc., is highly desirable. Strong ability in closed-form solutions and analytical methods development and understanding fundamentals in fluid mechanics. Utilizing DOE, Optimization, and statistical methods and data driven modeling to correlate Simulation data to experimental data. Predict, measure, and analyze the experimental data for uncertainty Quantification & propagation, sensitivity analysis, statistical inference for model calibration, decision making under uncertainty. Multi-scale modeling from nano, meso to macro levels Provide written reports and oral presentation of results to design teams and management. Work directly with mechanical, electrical, process and software engineers to define design requirements, goals and objectives of design, CIP, testing and simulation plans. Strong written and oral communication. Self-starter to start own initiatives and projects for continuous improvement in capabilities and design. Put your running shoes on: In this job you'll work in a highly dynamic and rapidly changing environment within a team of interdisciplinary experts driving to solutions to the most challenging business needs. PhD in Mechanical Engineering or closely related field with strong emphasis in Computational Fluid Dynamics, Heat transfer, Chemistry, or related fields with >6 years of experience in a related industry, e.g., semiconductor, gas turbine, aerospace, automotive, etc. Ability to work with a team to drive product development and design decisions. Propose design concepts and own decisions. Strong ability and understanding of AI/ML concepts and hybrid physics-based AI/ML modeling software. Building and maintaining codes of AI/ML models with either simulation or test data. Experience with machine learning algorithms and tools (e.g., TensorFlow, PyTorch, Scikit Learn etc.) and deep learning. Coding ability to supplement commercial software for specific applications as needs arise. Knowledge of chemistry, semiconductor metrology methods, and hardware designs in a vacuum environment is also a plus. General understanding of uncertainty quantification, Bayesian optimization and probabilistic machine learning is required. Ability to effectively communicate and build relationships to interact, inform, influence, and communicate with key stakeholders at all levels across the company. Strong critical thinking skills demonstrated through problem-solving, attention to detail and innovation. Strong analytical skills demonstrated through First Principles Thinking, statistical Analysis and Physics-based Insights