PhD student to the Machine Learning for Smart Mobility group

Wednesday 02 Sep 20

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Apply no later than 10 October 2020
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Shared, connected and cooperative automated vehicles may become a game changer for urban mobility. They can provide seamless door-to-door mobility of people & freight delivery services, which can lead to healthier, more accessible, greener & more sustainable cities, as long as they are integrated in an effective PT system. Since a few years the development of shared automated vehicle pilots are emerging around the world with the promise of great potential impacts on reducing CO2 emissions and pollutants, improving safety and reducing overall transport system costs. However, in order to be attractive and accepted by users, these systems and services need to be, among others, efficient, adaptive to user demand and robust to disruptions (e.g. caused by special events, inclement weather, incidents and road works). Fortunately, shared, connected and cooperative automated vehicles also constitute a unique Big Data source. This data, when coupled with the latest developments in Machine Learning and Artificial Intelligence in general, enables the implementation of smart and safe mobility solutions, innovative traveller services and (city) traffic management. 

The focus of this research is thus on leveraging state-of-the-art Machine Learning techniques to develop and implement novel solutions for improving the effectiveness, adaptivity and robustness of shared, connected and cooperative automated vehicles. This may include, for example, the development of: shared mobility demand prediction models accounting for the impact of external factors (e.g. special events, inclement weather, incidents and road works), robust and reliable arrival-time / travel-time prediction systems that are able to provide uncertainty estimates of their predictions, predictive routing approaches for a fleet of autonomous vehicles for optimally adapting supply to demand while also accounting for demand uncertainty, etc. 

Overall, this research lies in the intersection between Machine Learning (namely Deep Learning, Probabilistic Modelling and Bayesian Statistics) and Transportation Systems. The Machine Learning for Smart Mobility group of the Transport Division of the Department of Management at the Technical University of Denmark (DTU) is looking for excellent applicants to join the Division, starting on December 1, 2020 or earlier. 

This PhD is funded by the Horizon2020 project “Shared automation Operating models for Worldwide adoption (SHOW)”, which aims to support the migration path towards affective and persuasive sustainable urban transport, including shared, connected, coordinated autonomous vehicles. More details available here:  

We are looking for excellent applicants with MSc background on Computer Science, Transportation, Applied Mathematics, Statistics or related, and with the interest and ambition to pursue PhD studies. 


  • A MSc degree in Transportation Modelling, Computer Science, Applied Mathematics and Statistics or related is required;
  • Excellent programming capabilities in at least one scientific language (preferably Python) is required; 
  • Excellent background in statistics and probability theory is required;
  • Previous experience with Machine Learning is highly favoured; 
  • Transportation Modelling disciplines in the education background is also favoured;

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;

Approval and Enrolment
The scholarship for the PhD degree is subject to academic approval, and the candidate will be enrolled in one of the general degree programmes at DTU. For information about our enrolment requirements and the general planning of the PhD study programme, please see the DTU PhD Guide.   

The assessment of the applicants will be made by 20 October 2020. 

We offer 
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.

Salary and terms of employment
The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations. The allowance will be agreed upon with the relevant union. The period of employment is 3 years. 

You can read more about career paths at DTU here.  

Further information 
For more information, please contact Filipe Rodrigues, or Francisco C. Pereira,  

You can read more about the Machine Learning for Smart Mobility group at and DTU Management at   

Please submit your online application no later than 10 October 2020 (local time). Applications must be submitted as one PDF file containing all materials to be given consideration. To apply, please open the link "Apply online", fill out the online application form, and attach all your materials in English in one PDF file. The file must include: 

  • A letter motivating the application (cover letter)
  • Curriculum vitae
  • Grade transcripts and BSc/MSc diploma
  • Excel sheet with translation of grades to the Danish grading system (see guidelines and Excel spreadsheet here)

Candidates may apply prior to ob­tai­ning their master's degree but cannot begin before having received it.

Applications and enclosures received after the deadline will not be considered.

All interested candidates irrespective of age, gender, race, disability, religion or ethnic background are encouraged to apply.

The Machine Learning for Smart Mobility group belongs to the Transport division of the Department of Technology, Management and Economics (DTU Management) 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 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 introductory to advanced courses/projects at BSc, MSc and PhD level. The Department has a staff of app. 350. Read more here

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DTU develops technology for people. With our international elite research and study programmes, we are helping to create a better world and to solve the global challenges formulated in the UN’s 17 Sustainable Development Goals. Hans Christian Ørsted founded DTU in 1829 with a clear vision to develop and create value using science and engineering to benefit society. That vision lives on today. DTU has 12,000 students and 6,000 employees. We work in an international atmosphere and have an inclusive, evolving, and informal working environment. Our main campus is in Kgs. Lyngby north of Copenhagen and we have campuses in Roskilde and Ballerup and in Sisimiut in Greenland.