PhD candidate in 'Data-driven approaches towards robust and sustainable cold chains' (1.0 fte) within the Operations, Planning, Accounting and Control (OPAC) Group of the School of Industrial Engineering, in collaboration with the Eindhoven Artificial Intelligence Systems Institute (EAISI) and European Supply Chain Forum (ESCF).

The School of Industrial Engineering is one of the longest-established IE Schools in Europe,
with a strong presence in the international research and education community, especially in the field of Operations Management and Operations Research. OM and OR are at the core of the undergraduate IE program. The graduate programs (MSc and PhD) in Operations Management & Logistics attract top-level students from all over the world. Researchers in the school are member of the Beta research school and participate in industrial activities with members of the European Supply Chain Forum.

The group Operations, Planning, Accounting and Control (OPAC) conducts research in the area of Operations Management, with specific emphasis on

• Planning and Control in Manufacturing, Services, and Supply Chains;
• Distribution Logistics;
• Maintenance and Reliability;
• Finance and Accounting, oriented towards operational processes.

The Eindhoven AI Systems Institute (EAISI) combines all TU/e Artificial Intelligence activities. Top researchers from various research groups work together to create new and exciting AI methodologies and applications with a direct impact on the real world. TU/e has been active in the field of AI for many years, which gives the new institute an excellent starting position to build upon.

This PhD project is part of the AI PLANNER OF THE FUTURE program. This ambitious research program is hosted by the TU/e-based Department of Industrial Engineering & Innovation Sciences and is supported by the European Supply Chain Forum, Department of Industrial Engineering & Innovation Sciences, the Eindhoven Artificial Intelligence Systems Institute, and the Logistics Community Brabant. The program connects to the different communities, moonshots strategic agendas and the themes of each of these supporting partners. It combines 25 researchers, 10 PhD students and over 50 Bachelor and Master students, for the coming five years (2021-2026). This AI PLANNER OF THE FUTURE program considers the explicit intertwining of technical and human elements in the context of AI planning for supply chains and logistics, considering all relevant performance indicators (people, profit, and the planet).

The AI PLANNER OF THE FUTURE program
This ambitious research program is hosted by the TU/e-based Department of Industrial Engineering & Innovation Sciences and is supported by the European Supply Chain Forum, Department of Industrial Engineering & Innovation Sciences, the Eindhoven Artificial Intelligence Systems Institute, and the Logistics Community Brabant. The program connects to the different communities, moonshots strategic agendas and the themes of each of these supporting partners. It combines 25 researchers, 10 PhD students and over 50 Bachelor and Master students, for the coming five years (2021-2026). This AI PLANNER OF THE FUTURE program considers the explicit intertwining of technical and human elements in the context of
AI planning for supply chains and logistics, considering all relevant performance indicators
(people, profit, and the planet).

The following 10 individual PhD projects are embedded in this program. We are looking for
PhD candidates from a broad range of disciplines ranging from operations research and management, supply chain management, statistics, ethics, cognivite psychology, artificial intelligence, etc.

Project 1: Learning about Customers: Demand Implications of Logistics-Related Decision-Making in B2B, Gelper, Mutlu, Langerak https://jobs.tue.nl/en/vacancy/phd-on-marketingoperations-interface-878156.html

Project 2: Context matters: optimizing shared decision making in real-world forecasting and inventory management, Le Blanc, van de Calseyde, Ulfert  https://jobs.tue.nl/en/vacancy/phd-on-humanai-collaboration-at-work-878192.html

Project 3: AI-Based Replenishment and Order Fulfillment Strategies for Omnichannel Supply Chains, Atan , Schrotenboer, Van Woensel  https://jobs.tue.nl/en/vacancy/phd-on-aibased-replenishment-order-fulfillment-strategies-for-supply-chains-878193.html

Project 4: Robust data-driven sustainable food supply chain, Marandi, Rohmer, Van Woensel
PhD in 'Data-driven approaches towards robust and sustainable cold chains'

Project 5: Digital Twins: An ingenious AI companion or an evil twin?, Raassens, Schepers, Van Woensel https://jobs.tue.nl/en/vacancy/phd-in-ai-and-digital-twinning-878197.html

Project 6: AI for sustainable last-mile delivery by micromobility: a socio-technical perspective, Behrendt, Alkemade  https://jobs.tue.nl/en/vacancy/ai-for-sustainable-lastmile-delivery-by-micromobility-878198.html

Project 7 (Extra: 0.5 EAISI startup package + 0.5 ESCF): Data-driven Optimization using Digital Twins for Sustainable Last-Mile Delivery, Zhang, Bliek, Van Woensel
https://jobs.tue.nl/en/vacancy/phd-on-datadriven-optimization-for-sustainable-lastmile-delivery-878199.html

Project 8: Online Supply Chain Planning, Dijkman, Van Jaarsveld
https://jobs.tue.nl/en/vacancy/phd-in-information-systems-business-intelligence-878200.html

Project 9: From feared competitor to trusted companion: understanding and enhancing trust in AI over time, Snijder, Rooks, Willemsen  https://jobs.tue.nl/en/vacancy/phd-on-humanai-collaboration-in-the-workplace-trust-in-ai-over-time-878201.html

Project 10: Widening the frame: Rational choice beyond a given utility function, Müller 
https://jobs.tue.nl/en/vacancy/phd-on-%E2%80%9Cimproving-automated-rational-choice-through-metacognition%E2%80%9D-878202.html

Applicants should have completed (or be close to completion of) a Master's degree in mathematics, operations management, operations research, econometrics, industrial engineering, or a closely related discipline, with a solid background in mathematical methods. Fluency in English is required.

The project
Cold chains contain different types of uncertainty; from quality of supplied material to traffic congestion in last-mile delivery. These uncertainties often result in product losses and waste, aggravating the environmental footprint of the chain. As such, there is a need for robust policies that can address this uncertainty. Recently, we have seen how machine learning techniques, such as Neural or Kernel-based classifications, are used in Robust Optimization to derive robust policies by extracting important information from historical data. However, the computational complexity of these approaches still remains an issue.

In this project, we want to build on this recent stream of research and design approaches in data-driven robust optimization capable of tackling problems arising in cold chain settings. By using machine learning techniques within the framework of robust optimization we intend to then design robust policies that mitigate the negative effects of uncertainties that can be found within real-world settings such as the food system or pharmaceutical supply chains.

We expect the Ph.D. student to:

1. develop the project proposal based on academic literature;

2. combine machine learning approaches with robust optimization;

• build data-driven mathematical models using robust optimization;
• design algorithms to solve the built models;
• communicate the results with ESCF and EAISI;
• and present the findings at conferences and publish papers in internationally renowned journals.

• A meaningful job in a dynamic and ambitious university with the possibility to present your work at international conferences.
• A full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months.
• To develop your teaching skills, you will spend 10% of your employment on teaching tasks.
• To support you during your PhD and to prepare you for the rest of your career, you will make a Training and Supervision plan and you will have free access to a personal development program for PhD students (PROOF program).
• A gross monthly salary and benefits (such as a pension scheme, pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labor Agreement for Dutch Universities.
• Additionally, an annual holiday allowance of 8% of the yearly salary, plus a year-end allowance of 8.3% of the annual salary.
• Should you come from abroad and comply with certain conditions, you can make use of the so-called '30% facility', which permits you not to pay tax on 30% of your salary.
• A broad package of fringe benefits, including an excellent technical infrastructure, moving expenses, and savings schemes.
• Family-friendly initiatives are in place, such as an international spouse program, and excellent on-campus children day care and sports facilities.