The University of Luxembourg (UL) is a multilingual, international, research oriented young university.
The Transport Research Group within the Faculty of Science, Technology and Communication is looking for a PHD-student in Engineering Sciences (F/M)
- Ref : I2R-NET-PFN-13MAMB
· 3-year fixed-term contract, full-time (40h/week) with a possible prolongation of 1 year
· Student and employee status
· Beginning earliest April, 2014
The PhD-student will work under the guidance of an associate professor on a doctoral research project and will also contribute to education to a certain extent. She/he should finish the work with a doctoral degree in engineering science.
Research project: MAMBA – Multimodal Mobility Assistant (CORE project)
PhD research focus: Demand Estimation in Multimodal Transportation Networks
The PhD candidate will focus on the modeling aspects related to the simulation and performance of multimodal transportation networks, and in particular on the link between these models and the data collection and analysis performed by another PhD candidate within the FNR-CORE project MAMBA. The main scientific contribution will be the extension of the existing origin-destination (OD) matrix estimation techniques currently available only in single mode systems (car networks and transit networks) to a seamless multimodal transport network. The challenges envisaged are manifold. First of all, dealing with different heterogeneous data sources represents a challenge, but also an opportunity for integrating the information and translate it into underlying mobility patterns and likely OD flows that originated the observed data. Second, modeling multimodal networks implies simulating continuous and discrete transportation states and variables (e.g. continuous vs. frequency-based and scheduled-based services), which require specific modeling aspects that are not present in single-mode systems (among others, the decision making and interaction of the systems at the transferring nodes), and will certainly be a challenging problem in the inverse problem of inferring multimodal transport information to identify the most likely OD demand patterns. Third, the more complex modeling and behavioral aspects of multimodal systems (e.g., the ‘explosion’ of the choice alternative set, the difficulty in quantifying the costs for the transferring operations and decisions, etc.) will make the application on real-sized networks a great challenge.
The PhD candidate should possibly have knowledge of demand estimation techniques for traffic and transit networks, having experience with data analysis, data mining, pattern matching and identification techniques.
Your profile:
· Master degree in Engineering (strongly recommended field of transportation engineering) or in Computer Sciences
· Excellent background in network modeling (transportation networks especially)
· Knowledge in artificial intelligence (machine learning, data mining and analysis)
· Great skills in programming (Java, C++, Python, MatLAB)
· Willingness to inscribe as PhD-student at UL;
· Fluent written and verbal communication skills in English are mandatory, knowledge of another communication language such as French or German is also an asset.
· Commitment, team working, a critical mind, and motivation are skills that are more than welcome
We offer:
- Excellent research conditions in an international dynamic research-oriented environment;
- Necessary technology and software for pursuing the research;
- A motivating and interdisciplinary research team;
- Links with research on digital travel surveys (project iGear carried on at Security, Reliability and Trust) and on multimodal and sharing services (projects InCoMMune , MC-Career Integration Grant).
The University offers highly competitive salaries and is an equal opportunity employer.
Applications, written in English should be submitted online by 28th February 2014 and should include:
- Curriculum Vitae (including your contact address, work experience, publications);
- Cover letter indicating the research area of interest and your motivation;
- Copy of the MSc diploma;
- Transcript of all courses and results from the highest university-level courses taken;
The University of Luxembourg is an equal opportunity employer. For further questions, please contact Prof. Dr.-Ing. Francesco Viti (francesco.viti@uni.lu).