Postdoc: Develop the future charging infrastructure for electric vehicles

mandag 21 sep 20

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Frist 21. oktober 2020
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DTU hereby seek excellent candidates for a two-year Postdoc position at DTU, Denmark.

While increased use of electric vehicles (EV) represents one of the single most important means to attain greenhouse gas (GHG) reductions in the transport sector (Green Commission, 2019) it gives rise to an increasing need for a charging infrastructure for society. Today, there is a lack of scientific investigations and knowledge related to the planning of such infrastructure. In particular, it is not clear how evolving technologies such as increasing battery range, information technology and charging technology, will affect the need for, and composition of, such infrastructure. 

The Postdoc project will carry out high quality research to quantitatively investigate and qualify the type and extent of an EV charging infrastructure that is necessary and efficient, from the perspective of society. The overarching question of the project is whether it is possible to deploy a charging infrastructure which will both sufficiently and effectively meet the heterogeneous charging demand in a densely populated urban environment. 

The focus of the project is to leverage state-of-art technologies from mathematical modelling and simulation, possibly from the transport modelling area or from parallel quantitative areas, to be used for the complex problem of predicting charging demand and optimising charging supply.

The project may involve the following research topics;

  • The prediction of heterogeneous geographical EV demand: By applying agent-based model frameworks in combination with population synthesis models and by utilising existing high-quality mobility data and spatially organised data and networks all available at DTU.
  • Charging behaviour preferences, e.g. when and where to charge and price sensitivity.
  • The design of equilibrium models between charging demand (as represented by 1) and supply: By using M/G/c queuing models at the charger level and by utilising MSA algorithms as a mean to converge waiting time.
  • Charging supply optimisation: By using optimisation heuristics, such as genetic algorithms to swap supply formations or variants of facility location problems aimed at charging infrastructure.
  • Model and data integration for detailed geographical networks. Proximity to power-grid network and road-network are central for the location of chargers.
  • Smart-charging technologies and flexible pricing strategies to mitigate peak-loading.  

The impact of the project has the potential to be huge as it may directly help forming the future Danish Charging infrastructure with consequences for our ability to reduce CO2 and comply with ambitious climate targets. 

Overall, the research lies in the intersection between transport modelling and planning, and spatial simulation and optimisation. The project will be anchored in the Transport Demand Modelling Group of the Transport Division of the Department of Management at the Technical University of Denmark (DTU). The project will be co-supervised by the Department of Electrical Engineering to facilitate a close collaboration between the demand-side simulation and the supply-side optimisation. A PhD study will anchored in the Department of Electrical Engineering focusing on modelling the charging impact on the power system – and exploring the provision of grid and power system services. 

We are looking for an excellent applicant to join the Division, starting on 1 January 2021 or earlier. 

This Postdoc is funded by the EUDP project ‘FUSE”, which aims to support the planning of the charging infrastructure in the municipality of Frederiksberg and Copenhagen. The project consist of several industrial partners and data collection efforts that will be a benefit for the project. 

We are looking for excellent applicants with PhD background from Computer Science, Transportation, Applied Mathematics, Statistics or related, and with the interest and ambition to pursue a research career.  


  • A PhD 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;
  • Transportation Modelling disciplines in the education background is also favoured;
  • Power grid design and knowledge hereof is also favoured;
  • Experience with Machine Learning techniques is favoured; 

The following soft skills are also important:  

  • Curiosity and interest about current and future mobility challenges and EV charging in particular;
  • 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 carried out shortly after the deadline the 25 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 2 years. 

You can read more about career paths at DTU here

Further information 
For more information, please contact Jeppe Rich, or Peter Bach Andersen  

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

Please submit your online application no later than 21 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)
  • CV
  • Diploma (MSc/PhD)
  • List of publications

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 Transport modelling 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.

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