Lead Engineer – Probabilistic Design – Aerospace Research

Lead Engineer – Probabilistic Design – Aerospace Research

Requirements

• Doctorate degree in Mechanical Engineering, Aerospace Engineering with at least 3 years industrial experience, or related discipline OR Master’s degree in Mechanical Engineering, Aerospace Engineering, or related discipline with at least 8 years industrial experience
• Experience in probabilistic design, machine learning, and/or optimization of engineering components and systems
• Fundamental knowledge in probabilistic methods, machine learning, Bayesian methods, and optimization applied to engineering design problems
• Experience with leading government programs and proposal writing
• Fundamental understanding of solid mechanics and tools used in structural analysis such as ANSYS or similar FE software
• Ability to develop, modify and utilize custom computer codes in various languages such as Python, C++, Matlab, Visual Basic, Perl, R, etc
• Legal authorization to work in the U.S. is required. We will not sponsor individuals for employment visas, now or in the future, for this job opening
• Must be willing to work onsite in Niskayuna, NY

Responsibilities

• Collaborate with GE Aerospace design and services communities in the development of methods for probabilistic design, machine learning and optimization
• Apply probabilistic design, machine learning and optimization methods to real-world industrial applications for NPI design and Services maintenance planning for GE Aerospace business
• Implement probabilistic design, machine learning and optimization methods into GE internal design and services tools
• Train and coach GE engineers on probabilistic and machine learning methods and tools
• Lead and manage projects, people and funding

Preferred Qualifications

• In-depth understanding and methods development experience in dynamic Bayesian networks, Bayesian networks, physics-base/physics-informed forecasting, time-series modeling, image-based surrogates, probabilistic deep learning, transfer learning, physics discovery, uncertainty quantification, model calibration, verification & validation, DOE/DACE, metamodeling, sensitivity analysis, and inverse design
• Experience in solving complex engineering problems using probabilistic and machine learning methods above
• Experience with mechanical design and analysis methods
• Experience with software development
• Experience with fracture mechanics
• Demonstrated interpersonal, leadership and communication skills in a global team environment
• Strong interpersonal skills and analytical skills
• Ability to work across all functions/levels as part of a team
• Ability to work under pressure and meet deadlines
• Excellent written and verbal communication skills