Modeling Engineer 2 Thermal CFD AIML

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. 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; and 0-3 years professional experience. Strong ability and understanding of AI/ML concepts and hybrid physics-based AI/ML modeling software. Coding ability to supplement commercial software for specific applications as needs arise. 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 This is a graduate eligible position. Preferred knowledge of chemistry, semiconductor metrology methods, and hardware designs in a vacuum environment is also a plus. Ability to work within a team to own and design concepts and drive design decisions. Established skills in building AI/ML models using simulation data. Experience with machine learning algorithms and tools (e.g., TensorFlow, PyTorch, Scikit Learn etc.) and deep learning. General understanding of uncertainty quantification, Bayesian optimization and probabilistic machine learning is required.