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.

TU/e and the team

The PhD students will be supervised by dr. Layla Martin, dr. Nevin Mutlu and prof. dr. Tom Van Woensel. Nevin and Layla are both assistant professors focusing on revenue management in the retail and logistics sector. and operations research for transport and logistics, respectively. Tom is a full professor for freight logistics.
The project, the supervisors, and eventually also the PhD students are embedded in TU/e's Operations, Planning, Accounting, and Control (OPAC) group. OPAC uses methods from operations research and operations management on a wide variety of problems, and currently hosts around 50 PhD students from various backgrounds.
The supervisors are actively involved in the European Supply Chain Forum (escf.nl), a leading platform for collaboration between industry and academia on supply chain challenges. The project draws upon long-standing collaborations with industry. The PhD student will collaborate with several companies and municipalities/provinces.

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.

• 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.

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

• Full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months. You will spend 10% of your employment on teaching tasks.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. €2,901 max. €3,707).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• An allowance for commuting, working from home and internet costs.
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.