In the project ‘Lane Analysis and Route Advisor’, we will conduct research into solutions based on advanced data analytics that combine the integration of various data sources (‘big data’), AI methods such as machine learning (ML), natural language processing (NLP), semantic web techniques, and optimization techniques for prescriptive analytics and decision making. These methods will be applied to the optimization of route planning in global freight forwarding, initially targeted at air freight shipment with special handling needs. The project that forms the context for this position also involves intensive collaboration with several industrial partners and academic partners (CWI and VU). The candidate will work at the School of Industrial Engineering, TU Eindhoven. The research project involves the Information Systems group (IS) and the Operations, Planning, Accounting and Control group (OPAC).
We are looking for a researcher who should have completed (or be close to completion of) a PhD degree in Computer Science, Artificial Intelligence, Econometrics, or Industrial Engineering, with a solid background in quantitative research methods. An ideal candidate should have a good basis in machine learning, or natural language processing, or semantic web technologies, and is interested in applications in logistics.
PThe PhD position is part of the NWO funded project “MARCONI: Maritime Remote Control Tower for Service Logistics Innovation.” In this project, we aim to develop and demonstrate innovative service logistics concepts that exploit actual data on the state of maritime assets and the availability of the relevant maintenance resources. These concepts are aimed at (1) reducing maintenance costs, (2) increasing safety, by lowering the probability of unplanned system downtime and (3) reducing the number of unnecessary sailing movements (emissions) through smarter planning and/or clustering of maintenance activities. The ambition is to demonstrate the actual functioning of a remote service logistic control tower, with the long-term goal of developing and exploiting a scalable supply chain function in the maritime world. The PhD student will be a part of the research work-package on ‘Developing Service Logistics Decision Models’ led by TU/e. In the PhD project, there will be a close collaboration with the other partners of the MARCONI project: Boskalis, Damen, Gordian, Maastricht University, NLDA, Thales, RH Marine, Royal Netherlands Navy, and University of Twente.
See more information and apply here before March 10 2019: https://jobs.tue.nl/en/vacancy/phd-on-datadriven-maintenance-and-service-logistics-for-maritime-assets-455559.html