Creating a dynamic model
Postdoc (1 fte)
1 Postdoctoral researcher on Data Analytics for Trade Lane Risk Assessments and Control (1.0 fte) within the Operations, Planning, Accounting and Control (OPAC) Group of the School of Industrial Engineering.
- Den Dolech, Eindhoven, Noord-Brabant
- Tijdelijk contract / Tijdelijke opdracht
- Uren per week:
- 38 uur
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 part of 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 and consortia of research projects.
The group Operations, Planning, Accounting and Control (OPAC) conducts research in the area of Operations Management, with specific emphasis on
Research is generally quantitative in nature, while many of the researchers also engage in empirical research. All research is embedded in Beta, the KNAW-recognized research school for Operations Management & Logistics.
The project 'Data Analytics for Trade Lane Risk Assessments and Control' will conduct research into innovative solutions to optimize trade lane risk management for global pharmaceutical logistics. Together with our industrial partners, we aim to develop advanced data analytics solutions that can significantly improve current risk assessments and result in further trade lane optimization. The project focuses on the following two topics:
Topic 1: Machine learning for dynamic risk assessments. Today most lane risk assessments are based on theoretical assessments of supplier capabilities, collected from paper questionnaires or on-site audits, which may be combined with a set of sensor measurements from a small number of test shipments. In this task, we aim to improve this assessment method by using advanced machine learning methods on large historical shipments data sets.
Topic 2: Simulation-based optimization of trade-lane packaging options. Besides potential risks, another key consideration in setting up a new trade lane for temperature-sensitive products is the selection of appropriate packaging. In this task, we will develop a simulation tool that can simulate the internal product temperature during a specific trade lane.
We are looking for a junior researcher who should have completed (or be close to completion of) a PhD degree in Computer Science, Operations Management, Econometrics, or Industrial Engineering, with a solid background in quantitative research methods. An ideal candidate should have a good basis in machine learning, optimization and is interested in logistics.
The candidate will work at the School of Industrial Engineering, TU Eindhoven. The research project involves Information Systems (IS) and OPAC groups. We offer a researcher appointment for a period of 12 months with a starting date as early as possible.
More information about this position and the research programs should be addressed to: Dr. Alp Akcay (firstname.lastname@example.org) or Dr. Yingqian Zhang (YQZhang@tue.nl). Information about terms of employment can be obtained from the HR department (email@example.com), phone: +31 402478827. Further information about Eindhoven University of Technology can be found at www.tue.nl.
Your application must contain the following documents (all in English):
If you are interested, we invite you to apply as soon as possible. You can apply by pressing the 'apply now' button for this vacancy. We start interviewing as soon as possible and keep the position open until it has been filled.
Applications per email are not accepted. Please note that a maximum of 5 documents of 2 Mb each can be uploaded.