Open PhD Position - MOMENTUM Project (TSE109)

Chair of Transportation Systems Engineering (TSE)

The chair of Transportation Systems Engineering focuses on performing transportation research surrounding aspects of modelling and simulation of transportation systems, implementation of data science and data analytics in transport and human factors analysis. Specifically, the TSE chair performs research on both multimodal and unimodal freight and passenger transport demand and supply modelling, allowing for contributions on optimisation, calibration and validation of transport models. In this direction, the application of Big Data acquisition and analysis is examined as well as the use of Data-driven flexible models. Finally, the TSE chair contributes on the analysis of human factors analysis in transport-related fields such as road safety modelling, behavioural economics applications and modelling of factors that affect transportation systems user engagement.

Position Description

The position is part of the (Η2020 funded) MOMENTUM project, in which TSE is participating as a WP and task leader. Disruptive technologies, such as MaaS and CAVs, are bringing radical changes in urban mobility. The goal of MOMENTUM is to develop a set of new data analysis methods, transport models and planning support tools able to capture the impact of new transport options on urban mobility, in order to support cities in the task of designing the right policy mix to exploit the full potential of emerging mobility solutions. The specific objectives of the project are: 1. Identify a set of plausible future scenarios for the next decade to be taken into account for mobility planning in European cities, considering the introduction of disruptive technologies such as CAVs. 2. Characterise emerging activity-travel patterns, by profiting from the increasing availability of high-resolution spatio- temporal data collected from personal mobile devices and digital sensors. 3. Develop data-driven predictive models of the adoption and use of new mobility concepts and transport solutions, in particular MaaS and shared mobility, and their interaction with public transport. 4. Provide transport simulation and planning support tools able to cope with the new challenges faced by transport planning, by enhancing existing state-of-the-art tools with the new data analysis methods and travel demand models developed by the project. 5. Demonstrate the potential of the newly developed methods and tools by testing the impact of a variety of policies and innovative transport services in different European cities with heterogeneous sizes and characteristics, namely Madrid, Thessaloniki, Leuven, and Regensburg, and evaluating the contribution of the proposed measures to the strategic policy goals of each city. 6. Provide guidelines for the practical use of the methods, tools and lessons learnt delivered by the project in the elaboration and implementation of SUMPs and other planning instruments.


Candidates are required to:

  • Have an MSc degree in a relevant field (e.g. transportation engineering, data science, computer science)
  • Be enthusiastic about performing research on transport-related data analytics
  • Have a strong background in transportation modelling
  • Have strong analytical skills – e.g. statistics, machine learning
  • Be an analytical mind
  • Like programming
  • Have excellent working knowledge (written and oral) of English
  • Be able to work with strict deadlines
  • Experience with European funded projects will be considered as a plus

Additionally to the above, candidates must fulfil the TUM admission requirements 

Conditions of employment

TUM offers a competitive compensation package in accordance to Public Sector Collective Agreement of Länder (TV-L). This position is a 100% TV-L 13 initially funded for three years (A13 a.Z. or E13, depending on the circumstances of the successful applicant). The position expected start date is July 2019. More information on the offered wages can be found at

TUM is an equal opportunity employer. Qualified women are particularly encouraged to apply. Applicants with disabilities are treated with preference given comparable qualification.



Interested applicants who fit the requirements of the position are asked to send the following to

  • a CV
  • academic transcripts
  • a motivation letter
  • the names and contact information of three references

Please include the position ID in the email subject (e.g. [TSE109] and your name).

Review of applications will begin immediately and continue until the position is filled.