Aerospace Machine Learning Engineer - Surrogate Modeling
The candidate will support development and refinement of machine learning models to predict performance of aerospace vehicles. The engineer will train machine learning models from system data and observation. The machine learning model will feature rapid inference times and be employed as a surrogate to more expensive physics-based solutions of systems for the Department of Defense (DoD), and other customers. As new capabilities are needed, the engineer will support development and refinement of the models. The engineer will join a team of multi-disciplinary engineers to provide analysis and M&S of various airborne systems and subsystems. These models will be used to support design studies and real-time applications. The engineer will be responsible for:
• Developing surrogate modeling techniques for efficient approximations of aerospace vehicles
• Demonstrating, validating, and verifying surrogate model accuracy
• Support integration of government tools, commercial tools, and in-house surrogate modeling tools
About the Group: 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) real-time performance for hardware in the loop applications, and (5) multi-disciplinary design optimization.
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)
• 2+ years of experience with machine learning, response surface methods, kriging, or similar regression techniques
• Experience with transient, time-series predictive modeling
• Proficiency with the Python software languages
• Proficiency with version controlling through Git
Desired Qualifications:
• PhD in Aerospace Engineering, Computer Science, Mechanical Engineer, or similar discipline.
• Knowledge of the PyTorch machine learning library
• Knowledge of formal gradient-based and gradient-free optimization techniques
• Experience with multi-disciplinary analysis and optimization (MDAO)
Location: This role is based in the Huntsville, AL area, and is 100% onsite.
Aerospace Machine Learning Engineer - Surrogate Modeling • Huntsville, AL, United States
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