Research Computer Scientist - Artificial Intelligence Machine Learning

Analytical Mechanics Associates•Hampton, VA

3d•$100,000 - $150,000•Onsite

About The Position

In support of NASA Langley Research Center’s Aeronautics Systems Engineering Branch, AMA is seeking highly skilled candidates in the field of Artificial Intelligence / Machine Learning. Specifically, AMA is seeking candidates to support work within the uncrewed aircraft systems domain developing machine learning algorithms and tools based on the use of vision and/or radar sensors for airspace surveillance, onboard sense and avoid, and perception of hazards for autonomous navigation. The selected candidate will work with NASA and regulatory authorities on the overall advancement of machine learning with the aviation industry and increase NASA’s presence in the field. This position will be located on site at NASA LaRC.

Requirements

MS or PhD in Computer Science or related computational field with concentrations in artificial intelligence and machine learning
Demonstrated experience in machine learning through published research papers
Expertise in machine learning, computer vision, sensor-based data acquisition
Must work well in highly collaborative dynamic environments with diverse team
Strong communications skills, written and verbal
US citizenship or Permanent Residency is required

Nice To Haves

Application of machine learning with the aviation domain highly desired
Understanding of uncrewed aircraft systems operations highly desired

Responsibilities

Develop algorithms to detect, track, and classify uncrewed aircraft systems (multirotor, fixed wing, or VTOL), birds, general aviation vehicles, and commercial aviation vehicles
Integrate ground-based surveillance systems with airborne sensing suites by sharing data on an IoT platform
Conduct literature reviews to understand state of the art for machine learning, identifying gaps for aviation
Leverage previously collected NASA data along with future NASA and external data to evaluate machine learning techniques
Develop sensor fusion techniques for multiple sensor types using machine learning and traditional fusing techniques
Evaluate machine learning methodology using traditional machine learning metrics along with proposed aviation metrics by the Radio Technical Commission for Aeronautics (RTCA).
Assist in the design and execution of experiments at NASA Langley Research Center and Armstrong Flight Research Center
Assist in the development of machine learning challenges such as those for the Computer Vision Pattern Recognition Conference
Document research results for conference presentation and publication