Missile Modeling and Simulation (M&S) Engineer

CFD Research's Aerospace Data Science Group is developing a portfolio of traditional modeling and simulation and, machine learning tools for supporting aerospace R&D. This includes development of predictive machine learning and reduced-order models for (1) rapid estimation of aerospace vehicle properties; (2) optimal data collection; (3) affordable uncertainty quantification; (4) guidance navigation and control strategy development, and (5) multi-disciplinary design optimization. CFD Research is looking for a candidate that will support modeling, simulation, and analysis of missile systems for the Department of Defense (DoD), and other customers. The engineer will join a team of multi-disciplinary engineers to provide analysis and M&S of various airborne weapon systems and subsystems. The engineer will use 6 degree-of-freedom (6DOF) tools to simulate capabilities of airborne weapon systems to inform optimization. The engineer will use these M&S tools to provide and analyze data for a variety of systems and subsystems. The engineer will learn, utilize, and develop formal optimization processes for airborne weapon systems. As new capabilities are needed, the engineer will support development and refinement of the models. The engineer will also work with a team of machine learning engineers to train machine learning surrogate models which approximate the system capabilities in forms that can be rapidly evaluated. These models will be used to support design studies. Basic Qualifications: • Candidate must be a US Citizen and meet eligibility to obtain/maintain a Security Clearance • Position requires a Master's in Aerospace Engineering, Mechanical Engineer, Computer Science (or similar) • Experience with 6DOF trajectory simulation of missiles systems • Experience with guidance, navigation, and controls • Proficiency with Python and C++ software languages used in simulations • Proficiency with version controlling through Git Desired Qualifications: • Knowledge of surrogate modeling and machine learning libraries • Knowledge of formal gradient-based and gradient-free optimization techniques, particularly genetic algorithms • Experience with the CWS, CADAC, or AFSIM simulation tools • Experience with aerodynamic modeling tools like Missile DATCOM, CBAERO, OpenVSP • Experience with propulsion modeling • Experience modeling or simulating airborne weapon systems • Experience with multi-disciplinary analysis and optimization (MDAO) • PhD in Aerospace Engineering, Computer Science, Mechanical Engineer, or similar discipline. Location: This role is based in the Huntsville, AL area, and is 100% onsite.