Subasish Das

The Artificial Intelligence in Transportation (AIT) Lab at Texas State University is offering research assistant opportunities for motivated PhD and MS students, as well as outstanding undergraduates. The lab focuses on leveraging artificial intelligence and machine learning to address safety-critical transportation research challenges. Projects involve working with large-scale crash data, infrastructure data, multimodal mobility datasets, and developing AI-driven decision support tools for transportation systems. Core responsibilities include developing and benchmarking models for crash severity, driver behavior, and infrastructure risk, building end-to-end machine learning pipelines (from preprocessing to deployment), implementing explainable and trustworthy AI methods (such as counterfactuals, causal inference, and mechanistic AI), contributing to journals and conferences in transportation, AI, and data science, maintaining reproducible research-quality codebases for long-term use and open-source release, and collaborating with interdisciplinary teams across engineering, AI, and policy. Applicants should have strong foundations in artificial intelligence and software engineering, proficiency in Python for deep learning, experience with PyTorch and/or TensorFlow, knowledge of statistical learning, validation, and uncertainty analysis, and experience with large datasets (SQL, Pandas, NumPy) and spatial AI. Strong software practices (Git, documentation, experiment tracking) are required. Preferred qualifications include prior AI or research experience in computer science, statistics, transportation, or related fields, evidence of strong analytics (publications, preprints, competitions, open source), and for PhD applicants, independent methodological research capability, 2 Q1 publications, and 500+ GitHub stars. MS/UG applicants should have excellent coursework, a research mindset, and 1000+ GitHub stars. The research assistantship is renewable for 12–24 months based on performance. The specific stipend amount and tuition coverage are not mentioned. 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].

The Artificial Intelligence in Transportation (AIT) Lab at Texas State University is offering Graduate Research Assistant (GRA) opportunities for motivated M.S. students in Civil Engineering. The positions focus on transportation engineering and artificial intelligence, supporting safety-critical transportation research. Students will work on developing and benchmarking models for crash severity, driver behavior, and infrastructure risk, and will implement explainable and trustworthy AI methods such as counterfactuals, causal inference, and mechanistic AI. Collaboration with interdisciplinary teams across engineering, AI, and policy is expected. Applicants should have a strong foundation in transportation engineering and artificial intelligence, with proficiency in Python for deep learning and experience handling large datasets using SQL, Pandas, and NumPy. The GRE is a mandatory requirement for the application package. Preferred qualifications include prior AI or research experience in computer science, statistics, transportation, or related fields, and evidence of strong analytics such as publications, preprints, competitions, or open source contributions. The M.S. in Civil Engineering at Texas State University emphasizes practical, industry-driven projects or theses related to real-world engineering applications, often in partnership with local industries. Students will confront substantial, open-ended problems, perform detailed research, develop and implement solutions, and communicate their findings professionally. Funding is provided through GRA positions, though specific details regarding stipend or tuition coverage are not mentioned. Interested candidates should review the AIT Lab projects and apply via the official application site, ensuring that GRE scores are included in their application package. For more information, candidates may contact Professor Subasish Das or visit the provided links.

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.