<g class="gr_ gr_26 gr-alert gr_gramm gr_inline_cards gr_run_anim Punctuation multiReplace" id="26" data-gr-id="26">PhD</g> position on Data-Driven Maintenance and Service Logistics for Maritime Assets
<g class="gr_ gr_24 gr-alert gr_gramm gr_inline_cards gr_run_anim Grammar only-ins replaceWithoutSep" id="24" data-gr-id="24">Eindhoven</g> University of Technology has a vacancy for 1 <g class="gr_ gr_25 gr-alert gr_gramm gr_inline_cards gr_run_anim Punctuation multiReplace" id="25" data-gr-id="25">PhD</g> student on
Data-Driven Maintenance and Service Logistics for Maritime Assets (1.0 <g class="gr_ gr_23 gr-alert gr_spell gr_inline_cards gr_run_anim ContextualSpelling ins-del multiReplace" id="23" data-gr-id="23">fte</g>) within the Operations, Planning, Accounting and Control (OPAC) group of the School of Industrial Engineering.
The <g class="gr_ gr_29 gr-alert gr_gramm gr_inline_cards gr_run_anim Punctuation multiReplace" id="29" data-gr-id="29">PhD</g> position is a part of the MARCONI (Maritime Remote Control Tower for Service Logistics Innovation) project funded by the Netherlands Organisation for Scientific Research (NWO). 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 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 <g class="gr_ gr_30 gr-alert gr_gramm gr_inline_cards gr_run_anim Punctuation multiReplace" id="30" data-gr-id="30">PhD</g> student will work on the research work-package on
Developing Service Logistics Decision Models led by TU/e. In the <g class="gr_ gr_31 gr-alert gr_gramm gr_inline_cards gr_run_anim Punctuation multiReplace" id="31" data-gr-id="31">PhD</g> 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 <g class="gr_ gr_28 gr-alert gr_gramm gr_inline_cards gr_run_anim Grammar only-ins replaceWithoutSep" id="28" data-gr-id="28">University</g> of Twente.
Applicants should have completed (or be close to completion of) a Master's degree in operations management, industrial engineering, operations research, econometrics, statistics, computer science, data science, or a comparable domain with a solid background in quantitative research methods. The <g class="gr_ gr_36 gr-alert gr_gramm gr_inline_cards gr_run_anim Punctuation multiReplace" id="36" data-gr-id="36">PhD</g> candidate is expected to do scientific research in the domain described, write a <g class="gr_ gr_37 gr-alert gr_gramm gr_inline_cards gr_run_anim Punctuation multiReplace" id="37" data-gr-id="37">PhD</g> thesis and publish research results in scientific journals. The research is executed under the supervision of dr. Alp Akcay, dr. Yingqian Zhang, and prof.dr.<g class="gr_ gr_27 gr-alert gr_spell gr_inline_cards gr_run_anim ContextualSpelling ins-del multiReplace" id="27" data-gr-id="27">ir</g> Geert-Jan van Houtum. The candidate also participates in the <g class="gr_ gr_38 gr-alert gr_gramm gr_inline_cards gr_run_anim Punctuation multiReplace" id="38" data-gr-id="38">PhD</g> education program. A small part of the position involves teaching in the courses offered by the OPAC group.
For more information (including the application procedure), please see the attachment or click on the link:
https://jobs.tue.nl/en/vacancy/phd-on-datadriven-maintenance-and-service-logistics-for-maritime-assets-455559.html------------------------------
Alp Akcay
Assistant Professor
Eindhoven University of Technology
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