PhD Students and Postdoc Positions

Multiple Ph.D. and one Postdoc on Connected and Automated Vehicles (CAVs) and Intermodal Logistical Modeling at Michigan Tech

 

Multiple Ph.D. and one Postdoc positions are available at Michigan Tech. Prospective students will work on projects in the following two areas.

(1)    Connected and automated vehicles (CAVs): two Ph.D. students with research on modeling and on-road testing vehicular ad-hoc network (VANET) systems with Cellular V2X onboard units (OBUs) and roadside units (RSUs). Students’ backgrounds and interests are preferred in wireless network communication, optimization, automation, control, machine learning, data analytics, real-time systems, hardware-in-the-loop, systems integration, and software development. Students are expected to build VANET systems using C++ in Linux and travel to testing facilities to test connected and automated vehicles in the real world.

(2)    Intermodal logistical modeling: two Ph.D. students and one Postdoc. Students’ backgrounds and interests in intermodal logistics (maritime, rail, and truck), freight network modeling (e.g., routing and scheduling, network resilience), intermodal freight network simulation (e.g., operations on vessels on waterways, trains on railways, trucks on highways, charging stations, containers/railcars/trains/trucks at intermodal terminals, railcars/trains at classification yards, and containers/vessels/trucks/trains at ports), freight demand modeling (e.g., demand prediction and mode choice), renewable energy consumptions modeling for transportation (e.g., electricity and biofuel), EV charging behavior, queueing theory, decision-making under uncertainty, distributionally robust optimization, cooperative decision-making automation, Graph Neural Networks (GNN), data analytics, rolling horizon framework, and software development. Students are expected to develop mathematical models, intermodal data collection, model calibration, validation, testing, and system development using C++ in Windows.

 

Students are expected to work in the high-performance computing Laboratory on Sustainable and Intelligent Transportation Systems (SITS-Lab) with Dr. Kuilin Zhang. In the Transportation Systems area, Dr. Zhang's research focuses on transportation network modeling and optimization, Intelligent Transportation Systems, logistics and supply chain systems, traffic flow theory, traffic simulation, mobile sensing, big traffic data analytics, connected and automated vehicles, and plug-in electric vehicles. In the Computer Science area, Dr. Zhang's research focuses on Vehicular Ad-hoc Networks (VANETs), the Internet of Things (IoT), Cyber-Physical Systems (CPS), High-Performance Computing, Real-time Systems, Deep Reinforcement Learning (DRL), GNN, and Real-Time Machine Learning. Interested applicants from transportation engineering, applied mathematics, systems engineering, automation, communication, control theory, electrical and computer engineering, and computer science are encouraged to contact Dr. Zhang directly by sending a complete resume, transcripts, research statement, and representative publications to klzhang@mtu.edu, and submit their applications online at https://www.mtu.edu/cege/graduate/civil . You are expected to have

 

Dr. Kuilin Zhang is an Associate Professor in the Department of Civil, Environmental, and Geospatial Engineering (CEGE) in the College of Engineering and an Affiliated Associate Professor in the Department of Computer Science (CS) in the College of Computing at Michigan Tech, Houghton, Michigan, U.S.A. Dr. Zhang is also a faculty affiliate of the Michigan Tech Transportation Institute (MTTI), the Institute of Computing and Cybersystems (ICC), and the Power Systems Engineering Research Center (PSERC). Dr. Zhang received his Ph.D. in Transportation Systems Analysis and Planning from the Department of Civil and Environmental Engineering at Northwestern University in December 2009.  After working as a Postdoctoral Fellow in the Transportation Center at Northwestern, he joined the Energy Systems Division at Argonne National Laboratory as a Postdoctoral Appointee in November 2010. He joined Michigan Tech in August 2013. Dr. Zhang was a member of the Transportation Research Board (TRB) standing committees of Transportation Network Modeling (AEP40) and Freight Transportation Planning and Logistics (AT015), as well as a voting member of the Society of Automotive Engineers (SAE) Cooperative Driving Automation (CDA) Committee and BSM Task Force. He is also a member of IEEE, INFORMS, and ITE. Dr. Zhang's research has been supported by the National Science Foundation (NSF), the Department of Energy (DOE), the Department of Transportation (DOT), and the Michigan DOT (MDOT). He and his student also received the 2022 Gartner Prize for the "Best Paper on Traffic Flow Theory" from TRB ACP50 Traffic Flow Theory and Characteristics Committee. Dr. Zhang was a recipient of the NSF CAREER Award in 2019.