|Published||April 28, 2023|
|Location||Kgs. Lyngby, Denmark|
DTU Management’s Transport Division would like to invite applications for a 3-year PhD position. The successful candidate will join the Machine Learning for Smart Mobility Group and will work under the supervision of Associate Professor Filipe Rodrigues and Associate Professor Carlos Lima Azevedo.
This PhD project is part of a project entitled “Proactive traffic control through AI and Big Data”, funded by the Danmarks Frie Forskningsfond.
Reinforcement learning (RL) constitutes a promising solution for alleviating the problem of traffic congestion and associated greenhouse gas emissions by producing adaptive traffic signal controllers that outperform conventional systems. However, the existing solutions are merely reactive. This project aims at developing a new generation of proactive traffic control approaches by integrating into a RL framework a novel fully-Bayesian context-aware traffic prediction model that can forecast the future evolution of traffic and provide uncertainty estimates for its predictions, while accounting for contextual information (e.g. about planned events, incidents and weather), traffic network flow theory and the traffic signal control actions. The core idea is that, by accounting for the effects of external factors on future traffic conditions, the RL agent can learn to be proactive and take preemptive measures to alleviate (or even mitigate) future congestion scenarios and reduce emissions.
The successful candidate will explore new machine learning methods, particularly reinforcement learning, deep learning, and Bayesian modelling, for traffic signal control, which will be tested in highly realistic simulation scenarios to be implemented by the candidate in an existing open-source state-of-the-art mobility, energy and emissions micro-simulation tool during the initial stage of the PhD.
Together with our team (with a background in machine learning, reinforcement learning, Bayesian modelling and microscopic traffic simulation) the candidate will extend current microscopic traffic simulation models and then develop context-aware transport prediction models and model-based reinforcement learning solutions towards proactive real-time traffic signal control policies.
We are looking for excellent applicants with MSc background on Computer Science, Machine Learning, Transportation, Applied Statistics or related.
- Set-up realistic scenarios using an open-source state-of-the-art mobility, energy and emissions micro-simulation tool.
- Develop context-aware transport prediction models.
- Develop spatio-temporal models of the evolution of the traffic conditions given the traffic control actions.
- Implement and thoroughly test model-based reinforcement learning approaches for traffic signal control.
- Collaborate with researchers from machine learning and transportation in a truly interdisciplinary environment.
- Co-author scientific papers aimed at high-impact journals.
- Participate in international conferences.
- Take advanced classes to improve academic skills.
- Carry out work in the area of dissemination and teaching as part of the overall PhD education.
- A MSc degree (120 ECTS points) in Computer Science (Machine Learning or Simulation), Transportation, Applied Statistics or related.
- Great programming capabilities in at least one scientific language is required.
- Experience with machine learning is required.
- Good background in statistics and probability theory is required.
- Good programming capabilities in both Python and C, C++ or C# are preferred.
- Experience with reinforcement learning and/or traffic simulation is favoured.
The following soft skills are also important:
- Curiosity and interest about current and future mobility challenges and digital technologies.
- Good communication skills in English, both written and orally.
- Experience in writing and publishing scientific papers is an advantage.
You must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree.
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 DTU's rules for the PhD education.
The assessment of the applicants will begin on the 18 May 2023.
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 appointment terms
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. The position is a full-time position. Starting date is 1 August 2023 (or according to mutual agreement).
You can read more about career paths at DTU here.
If you are applying from abroad, you may find useful information on working in Denmark and at DTU at DTU – Moving to Denmark. Furthermore, you have the option of joining our monthly free seminar “PhD relocation to Denmark and startup “Zoom” seminar” for all questions regarding the practical matters of moving to Denmark and working as a PhD at DTU.
Your complete online application must be submitted no later than 16 May 2023 (Danish time). Applications must be submitted as one PDF file containing all materials to be given consideration. To apply, please open the link "Apply now", 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 (in English) including official description of grading scale
You may apply prior to obtaining your master's degree but cannot begin before having received it.
Applications 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 behaviour and 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 mission to develop and create value using science and engineering to benefit society. That mission lives on today. DTU has 13,500 students and 6,000 employees. We work in an international atmosphere and have an inclusive, evolving, and informal working environment. DTU has campuses in all parts of Denmark and in Greenland, and we collaborate with the best universities around the world.