Research Assistant in Dynamic Travel Behaviour and Machine Learning

A new postdoctoral associate position in artificial intelligence is available at Technical University of Denmark. The deadline to apply is January 31, 2019.

The Machine Learning for Smart Mobility Group and the Transport Modelling Division of the Department of Management Engineering at the Technical University of Denmark (DTU) is looking for excellent applicants to join the Division, starting on March 1st, 2019 or later.

The successful applicant will work on research focused on knowledge creation and new modelling methods for the human dynamic decision-making in mobility systems. We are looking for excellent applicants with MSc background on Transportation, Computer Science, Applied Mathematics and Statistics, Cognitive Sciences, Behaviour Studies, Urban Planning or, and with the interest and ambition to pursue PhD studies.

The focus of this research is in Behavioural Modelling lying in the intersection between, Artificial Intelligence and Choice Modelling.

Transportation systems are dynamic by nature. The system conditions are constantly changed by endogenous and external factors, affecting all travelling decision making. Incidents, congestion, unplanned events or even delays in performed activities ultimately affect trip departure time, mode, route and actual trip making choices. Smart mobility brings even more dynamic factors into the individual decision making process, with multiple responsive services and real-time information accessible at your fingertips through smartphone apps. Among dynamic behaviour in transportation, the concept of mid-term dynamics focuses on day-to-day learning of the effects of activities performed throughout a day and their effect, such that "successful" patterns are reinforced; and short-term dynamics are concerned with travel and activity-making responses to real-time conditions of travel networks and activity availabilities. Understanding how individuals change their behaviour from their initial plan is essential to creating the informative tools of the future for policy- and decision-makers which generally still rely on static assumptions. In this project you will focus on the development and understanding of the dynamics in individual decision making related to transportation. Both choice modelling classical methods and emerging machine learning techniques will be combined with stochastic process formulations to model such dynamics, especially focusing on scenarios with the introduction of a new smart mobility service in a given urban area. Different individual specific data sources, from paper to smartphone based travel surveys will be used in this project.

Literature review in dynamic behavioural processes, in order to generate a map of the current research and open problems; Design and estimate alternative formulations for selected dynamic decision making processes using an individual travel data set; Test the effectiveness of the frameworks based using simulation; A MSc degree in Transportation Modelling, Computer Science, Applied Mathematics and Statistics, Cognitive Sciences, Behaviour Studies, Urban Planning or related is required; Excellent programming capabilities, in at least one scientific language (e.g. Python, Matlab, R, Julia) is required; Excellent background in statistics and probabilities is required; Transportation Modelling disciplines in the education background are preferable; The following soft skills are also important: Curiosity and interest about current and future mobility challenges (e.g. smart and integrated mobility and travel behaviour); Good communication skills in English, both written and orally; Willingness to engage in group-work with a multi-national team; The assessment of the applicants will be made by 15 February 2019. DTU is a leading technical university globally recognized for the excellence of its research, education, innovation and scientific advice. We offer a rewarding and challenging job in an international environment. We strive for academic excellence in an environment characterized by collegial respect and academic freedom tempered by responsibility. The appointment will be based on the collective agreement with the Confederation of Professional Associations. The allowance will be agreed upon with the relevant union. The Research Assistant contract is offered for a one-year term. You can read more about For more information, please contact Carlos Lima Azevedo,, tel.: +45 4525 1545 or Francisco C. Pereira,

The Machine Learning for Smart Mobility group belongs to the of the department of Management Engineering at DTU. The division conducts research and teaching in the field of traffic and transport planning, with particular focus on behaviour modelling, machine learning and simulation. DTU Management Engineering conducts high-level research and teaching with a focus on sustainability, transport, innovation and management science. Our goal is to create knowledge on the societal aspects of technology – including the interaction between technology and sustainability, business growth, infrastructure and prosperity. Therefore, we explore and create value in the areas of management science, innovation and design thinking, business analytics, systems and risk analyses, human behaviour, regulation and policy analysis. The department offers teaching from introductionary to advanced courses/projects at BSc, MSc and PhD level. The Department has a staff of app. 350. Read more .


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