Postdoctoral Fellow (PREP0005052)

Postdoctoral FellowThis position is part of the National Institute of Standards and Technology (NIST) Professional Research Experience Program (PREP). NIST recognizes that its research staff may want to collaborate with researchers at academic institutions on specific projects of mutual interest and, therefore, requires those institutions to be recipients of a PREP award. The PREP program involves staff from a wide range of backgrounds conducting scientific research across various fields. Individuals in this position will perform technical work supporting the collaboration's scientific research. Research Title: Independent Evaluation of AI Decision-Making for Automated Vehicles The work will entail: The Measurement Science for Automated Vehicles (MSAV) project at NIST is seeking a candidate to support its effort on independent evaluation of AI-enabled decision-making in automated driving systems. This effort develops a measurement science framework and an open-source toolkit that evaluate ADS decision quality as a black box, across four progressive tiers (real-time safety monitoring, predictive outcome analysis, decision comparison against reference baselines, and systematic weakness diagnosis), without access to proprietary algorithms. The candidate will focus on two core areas: 1) simulation engineering for scenario-based ADS testing in CARLA and 2) software integration for the open-source evaluation toolkit and Evaluation Gateway. U.S. Citizen Preferred Key responsibilities will include but are not limited to: Simulation Engineering Manage the CARLA simulation infrastructure and build the scenario execution pipeline that runs OpenSCENARIO 2.0 test cases. Configure multi-agent traffic behavior using layered modeling (scripted, reactive car-following models such as IDM and MOBIL, stochastic, and learned agents) for large-scale evaluation runs. Integrate hardware-in-the-loop testing and optimize performance for large-scale multi-agent test runs. Support integration of open-source automated driving stacks (for example, Autoware) to test against diverse architectures. Software Integration Develop the open-source evaluation toolkit and design the Evaluation Gateway API and standardized interfaces. Implement the standardized behavioral data logging schema that defines the observable ADS output required for interoperable evaluation. Build the containerized deployment pipeline (for example, Docker) and the Cryptographic Hashing Message Schema enabling any ADS to connect as a black-box client without exposing source code, model weights, or training data. Produce automated reporting templates and technical documentation supporting reproducible, auditable evaluation.

Salary RangeThe referenced salary range represents the minimum and maximum salaries for this position and is based on Johns Hopkins University's good faith belief at the time of posting. Not all candidates will be eligible for the upper end of the salary range. The actual compensation offered to the selected candidate may vary and will ultimately depend on multiple factors, which may include the successful candidate's geographic location, skills, work experience, internal equity, market conditions, education/training and other factors, as reasonably determined by the University.