Le 21/11/2017 par vlehoux :

Description

As both historical and real-time data are available for more and more urban areas, integrating either one or the other in trip planning algorithms has received a wide interest in the literature in the past few years [Bast et al. 2014]. NAVER LABS is developing its own trip planning algorithms, pursuing research to improve models and algorithms, to take advantage of the various sources of data available in smart cites and to enhance user personalization and experience.

The aim of the internship is to join the Machine Learning and Optimization group for working on dynamic and time-dependent shortest path problems. Models and algorithms will be proposed and tested in the context of the current solution.

Requirements

As part of the 2nd year of your Master Degree, or final year of your Engineering School, you are looking for an internship lasting 5 or 6 months. Ideally, your diploma has a major in computer science and/or applied mathematics with some courses on operational research topics.

As prototypes are to be implemented, working knowledge of C++ is a plus and the candidate must be autonomous and motivated by optimization, innovation and research.

Bibliography

• [Bast et al. 2014] Hannah Bast, Daniel Delling, Andrew V. Goldberg, Mathias Muller-Hannemann, Thomas Pajor, Peter Sanders, Dorothea Wagner, and Renato F. Werneck. Route planning in transportation networks. Technical Report MSR-TR-2014-4, Microsoft Research, January 2014.

Start Date

Beginning 2017

Duration

5-6 months

Application instructions

Please send a resume, along with your last transcript of grade to both Darko Drakulic and Vassilissa Lehoux, firstname.lastname@naverlabs.com

http://www.europe.naverlabs.com/