Postdoctoral Appointee - AI/ML UAS Flight Dynamics/Controls, Hybrid

What Your Job Will Be Like We are seeking a Postdoctoral Appointee with a background in R&D AI/ML engineering, aerospace engineering, mechanical engineering, or atmospheric science to work with a multi‑disciplinary team to develop architectures to solve sensing and control problems related to turbulent atmospheric flows. The work will center around investigation of Unmanned Aircraft Systems (UAS) flight dynamics/control in turbulent flows including optimal control with model‑based and data‑driven approaches. On any given day, you may be called on to: Develop cutting‑edge machine‑learning approaches related to wind sensing, flight dynamics, and control performance including reinforcement and supervised learning for UAS flight in atmospheric turbulence. Post‑process simulated and measured results to assess quantities of interest for UAS flight. Work with a dynamic team of researchers, developers, experimentalists, and model analysts working to solve challenging multi‑physics problems. The selected applicant can work a combination of onsite and offsite work. The selected applicant must live within a reasonable distance for commuting to the assigned work location when necessary. This position is located in Albuquerque, NM, and the annual salary for this position is $98,500. Qualifications We Require PhD in engineering, physics, applied mathematics, computer science or other relevant field Ability to obtain and maintain a DOE L‑level security clearance Qualifications We Desire Demonstrated experience in flight dynamics and control including turbulence compensation Demonstrated experience with real‑time control applications including reinforcement learning and/or model‑predictive control Demonstrated experience with ML approaches for extracting features from large dataset Demonstrated experience coding in Python Ability to work in Linux environment with computing clusters Experience interfacing with drone controllers such as PX4 or ArduPilot Experience with distributed memory MPI programming Experience with collaborative software design, development, and testing processes Experience with the application of statistical methods, data analytics, or machine learning to enrich experimental or computational data sources Experience with visualization of complex 3D flow fields Experience modeling unsteady wind loads from atmospheric flows Knowledge of high‑fidelity flow simulation methods, actuator‑disc methods, and turbulence modeling approaches EEO All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or veteran status and any other protected class under state or federal law. #J-18808-Ljbffr