Member of Technical Staff, Applied AI

Required

• Three or more years building physics-based models of real engineering systems: simulations, reduced-order models, or first-principles analyses you set up yourself. You know which assumptions, boundary conditions, and mesh or discretization choices drive the answer, you have checked your results against experiments or measured data, and you can say what you did when they disagreed.
• Three or more years applying scientific machine learning to real physics or engineering problems, not synthetic benchmarks. You have used, validated and deployed learned models such as surrogates, neural operators, or physics-informed networks, and you know how they behave out of distribution: where they extrapolate badly, how you detected it, and when you fell back to the solver.
• Depth in one of the following engineering domains, with its codes and the standards it works to. You know where the models lie.
• Fluid dynamics and heat transfer: CFD, conjugate heat transfer, multiphase and two-phase flow, natural and forced convection.
• Thermodynamics and energy conversion: refrigeration and power cycles, heat exchangers, combustion, engines and turbines, HVAC.
• Electrical and power systems: power flow, interconnection and protection, power electronics and converter dynamics, electromagnetics.
• Electrochemistry and energy storage: batteries, fuel cells, corrosion.
• Solid mechanics and materials: FEA, vibration, fatigue, materials degradation.
• Transport and environmental physics: atmospheric dispersion, hydrology and water systems, subsurface flow, fire.
• Chemical and process engineering: reaction kinetics, process simulation, working fluids.