Postdoctoral Fellows and Full-Time Research Assistants

The Mobility Science Lab (MoS Lab) at Tsinghua University is recruiting postdoctoral fellows and full-time research assistants. We study optimization, machine learning, and their applications in transportation and operations management. We welcome applicants committed to rigorous research and publication in leading international journals.

Open Positions

Postdoctoral Fellows

  • The position focuses on high-impact research and scholarly publication. Fellows will conduct independent and collaborative research within the lab’s areas of interest.
  • Eligible candidates will receive full support in applying for Tsinghua University’s Shuimu Tsinghua Scholar Program.
  • For outstanding candidates who qualify for the Shuimu Tsinghua Scholar Program but are not selected, the principal investigator will provide compensation matching or exceeding the program’s salary level.

Full-Time Research Assistants

  • This is a research-focused position comparable to a Research Scientist role at a U.S. university or research institution, with scholarly research and publication as its primary responsibilities.
  • Depending on their interests and expertise, research assistants will independently or collaboratively develop research projects and contribute to problem formulation, modeling, computational experiments, and paper writing.
  • Compensation will be determined based on the candidate’s research record, experience, and overall qualifications. In general, it will be no lower than the salary level of the Shuimu Tsinghua Scholar Program.

Research Areas and Target Journals

  • Transportation system resilience and operations optimization: disruption management, resource allocation, path recommendation, robust optimization, and intelligent scheduling.
  • AI for Transportation: reinforcement learning, foundation models for time-series forecasting, transportation management agents, and AI applications in public transit, shared mobility, and supply-chain logistics.
  • Travel behavior and demand modeling: econometrics, discrete choice models, modern machine learning, and optimization-driven behavioral and demand analysis.
  • Sustainable urban systems: commuting emissions, public health, housing mobility, and the resilience of urban cyber-physical-social systems.

Our research is driven by publication in leading international venues, with a primary focus on UTD24 journals, Transportation Science, and Transportation Research Part B, as well as other leading journals and conferences in transportation, operations management, artificial intelligence, and interdisciplinary research.

Principal Investigator

Baichuan Mo

Baichuan Mo is a tenure-track Assistant Professor in the Department of Transportation Engineering at Tsinghua University and the director of MoS Lab.

He received his bachelor’s degree in Civil Engineering from Tsinghua University in 2018, dual master’s degrees in Transportation and Computer Science from MIT in 2020, and a PhD in Transportation from MIT in 2022. He was awarded the MIT UPS PhD Fellowship, and his dissertation received multiple honors, including the COTA Best Dissertation Award. In 2024, he was selected for a national-level young talent program in China.

He has published more than 30 papers in journals including Nature Sustainability, Transportation Science, Transportation Research Parts A/B/C/E, IEEE Transactions on Intelligent Transportation Systems, and the European Journal of Operational Research. Before joining Tsinghua, he was a Senior Research Scientist at Lyft and later a Staff Machine Learning Engineer and U.S. Supply Chain Algorithm Lead at ByteDance/TikTok E-Commerce, bringing experience in both academic research and large-scale algorithm deployment.

See his full faculty profile.

Qualifications

  • A background in operations research, management science, transportation, computer science, statistics, applied mathematics, or a related field.
  • Strong foundations in mathematical modeling, optimization, machine learning, econometrics, or data analytics.
  • Demonstrated research ability and strong academic reading and writing skills in English, with a commitment to producing high-quality scholarly work.
  • Postdoctoral applicants should hold, or expect to receive soon, a doctoral degree. Qualifications for full-time research assistants will be evaluated holistically based on educational background and research experience.

Application Materials

Please email the following materials to bmo[at]tsinghua.edu.cn:

  1. Curriculum vitae;
  2. Full texts of representative papers;
  3. Three letters of recommendation.

Please use the subject line “Postdoctoral Application — Your Name” or “Full-Time Research Assistant Application — Your Name.”