Project Description
This project explores the use of innovative and emerging applications of AI/ML (e.g., Natural Language Processing) to digitize and analyze heritage air traffic management (ATM) datasets. Given the rich volume of available text, audio and track data and the complexity of the problem space, this is an ideal candidate for the application of machine learning. The research is… focused on harvesting semi-structured or unstructured information contained in Letters of Agreement (LOAs) and enabling automation (e.g., Traffic flow management) by extracting and sharing the digitized information. This information will be used by various National Airspace System (NAS) users to optimize current day flight operations e.g., to perform runway configuration management and analyze Traffic Management Initiatives (TMIs). The work will help build semi-automated recommender services that can assist complex decision making currently made by FAA specialists and subject matter experts. Interns will develop a project plan with scope, milestones, and deliverables to construct a data pipeline that finds language patterns for classification and extraction of useful data. Interns will work collaboratively with NASA personnel and other government organizations. Prefer background and/or interest in data science, statistical analysis, speech recognition, and software development. Computer Skills: Python Strong background and/or interest in: Data Science Statistical Analysis Artificial Intelligence and Machine Learning Automatic Speech Recognition Software development Cloud-based services AI/ML, Aviation, especially Airline Operations, Air-Traffic Management or Control Other Skills: Ability to work in virtual environment Ability to collaborate with others Willingness to learn and adapt based on team needs Self-starter and ability to work independently to deliver products Must be U.S. Citizen or permanent resident.
Academic & Other Information
Academic Level
11;12
To apply for this job please visit topsiksha.com.
