Ongeveer 7 uur geleden - Maastricht University (UM) - Maastricht
The successful candidate will be a part of the BONE Consortium, a recent North Western Europe Inter-Regional (NWE INTERREG) initiative with the objective of de…
The Departments of Industrial Engineering (IE) of the Eindhoven University of Technology invite applications for two four-year Ph.D. positions in the area of data-driven maintenance logistics. The intended starting date for these positions is ...
The Departments of Industrial Engineering (IE) of the Eindhoven University of Technology invite applications for two four-year Ph.D. positions in the area of data-driven maintenance logistics. The intended starting date for these positions is Nov 1, 2017 and can be further negotiated with interested applicants.
The positions are embedded in the NWO-funded project 'Real-time data-driven maintenance logistics', which includes several industrial partners such as NS, Philips and Fokker. PhD candidates will be involved in research with these companies, as well as with the Mathematics & Computer Science department at TU/e, and the Computer Science department at Delft University of Technology.
Companies in maintenance logistics aspire to transition from traditional static maintenance logistics plansbased on rigid task intervals to dynamic maintenance logistics policies fuelled by real-time data. This project aims at developing efficient algorithms and analytical tools that enable companies in the service logistic sector to better plan and control dynamic planning. The developed ICT prototype integrates real-time data from smart assets with maintenance planning, while providing support to the human operator, when dealing with complex decisions based on huge streams of heterogeneous data.
The project is divided into two key work packages (WP):
WP1 is focused on integration of machine learning models and real-time decision making. We will use techniques from machine learning, optimization modelling, and simulation to develop new methods for learning predictive models that explicitly consider the decision making context as well as real-time decision algorithms that integrate those predictive models.
WP2 is focusedonintegration of real-time decision support into semi-structured, data-driven processes. We will use methods from business process management and stochastic decision making to develop a maintenance logistics control system that supports human decision makers in dynamically organizing logistics based on the actions that are generated in real-time by WP1.
The projects will be carried out at the Information Systems (PhD1) and the OPAC (PhD2) groups from the IE department of TU/e. IE is one of the longest-established IE Schools in Europe, with a strong presence in the international research and education community. Researchers in the school are member of the Beta and SIKS research schools, and participate in industrial activities with members of the European Supply Chain Forum.
The two PhD candidates will be supported and supervised by an extensive research team of experienced faculty and industrial partners. At OPAC group, Prof. Geert-Jan van Houtum (firstname.lastname@example.org), Dr. Willem van Jaarsveld (w.l.v.Jaarsveld@tue.nl), Dr. Alp Akçay (A.E.Akcay@tue.nl), and at IS group, Prof. Uzay Kaymak (email@example.com), Dr. Rik Eshuis (H.Eshuis@tue.nl), Dr. Yingqian Zhang (firstname.lastname@example.org) will be involved. Further collaboration with researchers from NS, Philips, and Fokker, is expected, as well as collaboration with researchers working on this project at M&CS of TU/e and Delft University of Technology.
The two positions, for WP1 and WP2 respectively, are related, but have different profiles. The first position will be filled preferably by a candidate in Artificial Intelligence/Computer Science, Operations Research, or Econometrics, with a strong and verifiable interest in machine learning and mathematical optimization.
For the second topic, preference will be given to candidates in Industrial Engineering, Computer Science, or any branch of applied mathematics. Candidates with a strong and verifiable interest in stochastic optimization and/or business process modelling are preferred. Research experience will be highly valued.
All applications should include a cover letter, curriculum vitae, and transcripts. Proficiency in English is also required.
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