Assistant Research Scientist (PREP0003947)

PREP Research Associate – Overview This role is part of the National Institute of Standards and Technology (NIST) Professional Research Experience Program (PREP). The selected candidate will be part of a team of engineers and researchers conducting research and development activities focused on accelerating the development and deployment of real‑world test methodology to evaluate communications protocols in Uncrewed Aircraft Systems (UAS) for first responder solutions. Key Responsibilities Conduct literature reviews and document criteria for drone communications simulator solutions and the datasets used to train and test them. Prepare an industry landscape identifying long‑range communications, mesh networking, and best practices on developing methodologies for communications technologies. Develop a software program for a new drone communications simulator platform or retrofit an existing platform, integrating radio hardware communications components. Create and collect measurement data for the drone simulator. Document the dataset curation process for public use and determine target public safety datasets and enhancement options. Participate in public safety working groups and stakeholder testing to compare simulations with live UAS flights and curate shared information from collaborators. Evaluate drone simulator use cases and identify technology challenges and gaps to address. Qualifications Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field. Knowledge of network communications, routing, and radio signaling. Knowledge of robotics, UAS, or autonomous software programming (PX4, ArduPilot, AirSim, MAVLink). Knowledge of software engineering for virtual simulation applications. Experience with modern AI tools and applications (AWS, GCP, Azure, MLFlow, SQL, BigQuery, Snowflake). Experience with development and manipulation of software code (Python, C, C++, Qt, JavaScript). Knowledge of data structures, formats, and software coding curation processes. Experience with developing open‑source code, identifying use cases, and evaluating dataset quality and representativeness. Knowledge of mathematical probability and statistics, and optimization methods. Knowledge of machine learning, including supervised and unsupervised learning, deep learning, and model evaluation. Knowledge of dataset biases and labeling issues. Knowledge of translating operational needs into solvable radio signal or networking problems. U.S. citizen preferred. Equal Opportunity Employer The Johns Hopkins University is committed to equal opportunity for its faculty, staff, and students. The university does not discriminate on the basis of sex, gender, marital status, pregnancy, race, color, ethnicity, national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status or other legally protected characteristics. The university is committed to providing qualified individuals access to all academic and employment programs, benefits and activities on the basis of demonstrated ability, performance and merit without regard to personal factors or demographic characteristics that are irrelevant to the program involved. #J-18808-Ljbffr Johns Hopkins University