The Artificial Intelligence in Transportation (AIT) Lab at Texas State University is offering research assistant opportunities for motivated PhD and MS students interested in applying artificial intelligence to transportation systems. The lab focuses on safety-critical transportation research, leveraging large-scale crash data, infrastructure data, multimodal mobility datasets, and AI-driven decision support tools. Research topics include crash severity modeling, driver behavior analysis, infrastructure risk assessment, and the development of explainable and trustworthy AI methods such as counterfactuals, causal inference, and mechanistic AI.

Core responsibilities involve developing and benchmarking machine learning models, building end-to-end ML pipelines, implementing reproducible research-quality codebases, and collaborating with interdisciplinary teams across engineering, AI, and policy. Candidates are expected to contribute to journals and conferences in transportation, AI, and data science, and maintain open-source codebases for long-term use.

Applicants must demonstrate strong foundations in artificial intelligence and software engineering, with proficiency in Python, deep learning frameworks (PyTorch and/or TensorFlow), statistical learning, and large dataset handling (SQL, Pandas, NumPy). Experience with reproducible workflows (Git, documentation, experiment tracking) and spatial AI is required. Prior Q1 journal publications and open source contributions (1,000+ GitHub stars) are necessary. Preferred qualifications include prior research experience in computer science, statistics, transportation, or related fields, strong analytics evidenced by publications or competitions, and for PhD applicants, independent methodological research capability, 2 Q1 publications, and 500+ GitHub stars; for MS/UG applicants, excellent coursework, research mindset, and 1000+ GitHub stars.

The assistantship is renewable for 12–24 months based on performance. The start date is flexible. Interested candidates should send a brief introduction, CV, and links to GitHub, Google Scholar, or prior work (if available) to Dr. Subasish Das at [email protected]. For more information about the lab's projects, visit the AIT Lab website.