JOB DESCRIPTION

Logistics activities are major contributing factors to the construction sector’s emissions. While synchromodal networks (combining road transport with alternative modes of transport) have shown significant benefits for cost and emission reduction in long-haul transport, they have not seen sufficient uptake in the construction industry where the national borders usually confine the flows. The high weight and volume that characterize construction and demolition cargo make water transportation an ideal candidate for reducing emissions.
The DINALOG project “SYNERCIZE” (Synchromodal Transport Networks for a Construction Industry toward Zero Emissions) fosters long-term sustainable modal shift toward zero-emission waterways by (i) developing digital tools, (ii) optimizing and coordinating the day-to-day operations, (iii) designing networks which easily adapt to changing demand, and (iv) developing business models to support this shift. At TU/e, we will be working on the optimization and coordination of operations as well as the network design. We thus invite applications for two PhD students who are interested in working at the intersection of research and practice in collaboration with municipalities as well as construction companies, who like to further the state-of-the-art in decision-making algorithms for construction logistics, and who are eager to work toward a zero-emission construction industry.
Both PhD students will utilize and develop various methodologies from operations research and operations management, including combinatorial optimization, (deep) reinforcement learning, game theory, and simulation.

The first PhD student will work on designing flexible transport networks, which can include research on

• Network design under multiple objectives such as cost and emissions.
• Planning the development of synchromodal networks over time.
• Policy-making to ensure synchromodal networks see sufficient uptake.

The second PhD student will work on operating flexible transport networks, which can include research on

• Algorithms for routing and scheduling trucks and barges.
• Algorithms for the integrated charging and operation of electric push barges.
• Decentralized decision-making algorithms and incentive/pricing schemes for multiple decision-makers in the construction logistics chain.

TARGET PROFILE

We are looking for PhD students with a keen interest in operational research methods, solid understanding of the logistics and/or construction industry, and a drive to contribute to reducing greenhouse gas emissions.
Students shall have prior experience in optimization, operations research, decision support, and/or artificial intelligence, for example proven by a suitable master degree (e.g., Industrial Engineering, Operations Research, Econometrics, Computer Science, Applied Mathematics).
Candidates must have experience in object-oriented programming, using languages such as C++ or Java. Additionally, experience in Python is beneficial.
Ideally, candidates have experience with mathematical modeling and implementation in CPLEX or Gurobi, implementing efficient heuristics, and/or machine learning.
Ideally, applicants have already finished their master degree or are expected to finish soon. We envision that at least one of the two candidates starts in Spring 2025, with the second one following by September 2025 the latest.

JOB REQUIREMENTS

• A Master’s degree in Industrial Engineering, Operations Research, Econometrics, Computer Science, Applied Mathematics, or a related field.
• Prior experience with operations research methods such as optimization, machine learning, etc.
• Strong analytical and mathematical skills.
• Programming experience in object-oriented languages such as C++ or Java.
• Excellent verbal and written communication skills in English.

ABOUT US

Eindhoven University of Technology is an internationally top-ranking university in the Netherlands that combines scientific curiosity with a hands-on attitude. Our spirit of collaboration translates into an open culture and a top-five position in collaborating with advanced industries. Fundamental knowledge enables us to design solutions for the highly complex problems of today and tomorrow.
Curious to hear more about what it’s like as a PhD candidate at TU/e? Please view the video

Please refer the Job description for details