Skip to content  

Working at TU/e

Assistant Professor Big Data and Transportation

Assistant Professor Big Data and Transportation

The Department of the Built Environment has a vacancy for an Assistant Professorship in the Urban Planning and Transportation Group within the unit Urban Systems and Real Estate (USRE). The open position is part of the Design and Decision Support System (DDSS) research program.
Position
Assistant Professor
Department(s)
Built Environment
Strategic area(s)
Energy, Smart Mobility
FTE
1,0
Date off
30/09/2021
Reference number
V38.5072

Job description

We are on the verge of digital society which comes with the omni-presence of information and communication technology.  Modern communication technology also led to new platform economies. Platforms easily bring together different stakeholders in an integrated setting and assist in organizing business transactions. Car-sharing services are nowadays often based on a platform that keeps track of available cars and allows subscribers to make reservations and pay through traditional bank and credit card channels and modern equivalents such as PayPal, bitcoin, Apple pay, etc. Ride sharing services such as Uber are based on the same technology and business principle. The latest development in this context is the Mobility-as-a-Service concept, in which subscribers can use different transportation modes available in the bundle using discounted fares.

In order to operate the above innovative mobility options optimally, stakeholders collect data of different kinds on use and position of their transport supplies and movement patterns of citizens by gadgets installed on the vehicles as well as tracking smart cards swiping. Similarly, city authorities collect various types of data over time such as traffic flow, emission and so on. Likewise, telecommunication providers have massive amount of data on mobile internet usage and calls which can also be used to shed light on the mobility of citizens. Since these data are typically collected over a long period of time and lacks detailed information, they are sometimes referred to as “long and thin” data.

While a tremendous amount of data is collected on the daily basis, without knowledge of how to use this data for planning, design and operating of our built environment in more efficient ways and along citizens’ need, the effort in collecting those data will be in vein. Traditional travel demands forecasting models have typically been developed on travel surveys collected at the national level. Such data collection is becoming increasingly obsolete due to the high cost and low response rate especially when long term data (multiple days/ weeks) is desired. In this context it is essential to leverage big data in updating such demand forecasting models by exploiting its strength and avoiding its shortcomings (lack of details).

The use of mobility big data for planning and forecasting purposes requires dedicated knowledge on data analysis methods (data mining, deep learning, unsupervised and semi-supervised learnings) and their relations with the statistical methods used traditionally for the analysis of survey data (discrete choice analysis, conjoint analysis). Such knowledge can purposedly contribute to the development or update of decision support tools. Tools which can serve multitude of purposes, from long term to mid and short term planning. Optimum locations of newly built charging stations,  shared vehicles relocation strategies, optimum dynamic pricing for electricity, demand for the first and last mile mobility options in high temporal and spatial resolution, among others, can then be devised, supported by a data driven decision tools. To be able to realize the above purposes, the candidate should have a proven background on the following areas:

  • Data mining methods to analyse mobility big data
  • Travel demand forecasting models, including tour based and activity based models
  • Discrete choice methods and conjoint analysis
  • Stated choice experiment design

In addition to research, the candidate is expected to be highly involved in the educational tasks. The candidate is expected to contribute to teaching and supervision of courses in the Bachelor of Architecture, Urbanism and Building Sciences (AUBS) and Master of Architecture, Building and Planning (APB). Courses such as “Big data for urban analysis”, “Smart urban environments” and “mobility and logistic” among others need to be supported by the candidate. To that end, the applicant must have a track record in teaching and supervising of Bachelor and Master students.

Job requirements

  • You hold a PhD Degree in the field of urban or transportation planning or in a related field;
  • You have demonstrated your research skills by means of publications in reputed journals and conference proceedings.
  • You have gained international experience and collaborated internationally on the vacancy topic.
  • You can create a vibrant and effective learning environment and have the ability to motivate students both in research-based and challenge-based types of education (academic proficiency and design and engineering attitude).
  • You have a track record in teaching and supervising students.
  • You have a supportive international network in academia and industry, which will help you to acquire research funding.
  • You have a good command of English, both orally and in writing.
  • You are able to work collaboratively as well as independently.

Conditions of employment

  • A meaningful job in a dynamic and ambitious university with the autonomy to develop your own research line and participate in the curriculum of the department;
  • A Tenure Track of five years with the prospect of becoming an Associate Professor. After a maximum of four years, the tenure decision will be made. If you have a more senior profile, you will receive a tailor-made career proposal.
  • You will have free access to high-quality training programs for academic leadership, the university teaching qualification program, research and valorization competences, and a dedicated mentoring program to help you get to know the university and the Dutch (research) environment;
  • Salary is based on knowledge and experience and is at least € 3.746 up to a max. of € 5.127 gross per month (salary scale 11) at a fulltime appointment;
  • Additionally, an annual holiday allowance of 8% of the yearly salary, plus a year-end allowance of 8.3% of the annual salary;
  • A broad package of fringe benefits, including an excellent technical infrastructure, moving expenses, and savings schemes;
  • Family-friendly initiatives are in place, such as the Dual Career Opportunity program to support accompanying partners, an international spouse program, and excellent on-campus children day care and sports facilities.

Information and application

For further information on this vacancy you can contact
prof.dr. Soora Rasouli, phone number +(31)40–45273315, e-mail: s.rasouli[at]tue.nl.

For information concerning employment conditions click here or contact Marcel Vogels,
phone number +31 (0)40 247 3144, e-mail: m.vogels1[at]tue.nl

Please visit www.tue.nl/jobs to find out more about working at TU/e!

Application:
If you are interested in this position, you can only use the 'apply now'-button on this page.
We do not respond to applications that are sent to us in a different way.

Your application must contain the following documents:​​

  • Cover letter in which you describe your motivation and qualifications for the position (see above).
  • Curriculum vitae, including a list of your publications and the contact information of two references,
  • List of five self-selected ‘best publications’,
  • Description of your scientific interests and plans (1-2 pages),
  • Statement of your teaching goals and experience (1-2 pages)
  • Copy (or a most recent draft version) of your MSc thesis.
  • Transcripts of academic records indicating courses taken, including grades,
  • Contact details of two references (e-mail, phone number).

Please keep in mind you can upload only 5 documents up to 2 MB each. If necessary please combine files.