PhD scholarship in Modelling and Simulation of Demand Responsive Electrified Multimodal Transit Syst

DTU Management’s Transport Division would like to invite applications for a 3-year PhD position starting no later than 1 January 2023. The successful candidate will join the Machine Learning for Smart Mobility Group and will work under the supervision of Associate Professors Carlos Lima Azevedo and Filipe Rodrigues.

The successful applicant will work on research which is focused on the development of next-generation large-scale mobility simulation and the use of new machine-learning techniques in the design of combined energy-transportation demand management schemes.

We are looking for excellent applicants with MSc background in Computer Science, Transportation, Applied Mathematics, Environmental or Electric Engineering and Operations Research with the interest and ambition to pursue PhD studies in the field of applied simulation and machine learning.

This position is funded under the DTU-KTH Alliance and a collaboration with Professors Erik Jenelius and Zhenliang Ma, from the Department of Civil and Architectural Engineering of KTH Royal Institute of Technology, Sweden, is planned including a 6 month stay at KTH.

Project Background

Recent technological and business advances have supported the rise of new shared on-demand mobility services (such as ridesharing, micro-mobility, etc) that promise opportunities for improved accessibility. However their integration with classical transit systems continue to challenge the existing planning and operations and to understand the full potential gains in welfare for complex transportation systems of the future. Beyond these, the increasing electrification of transport requires a holistic approach to planning and managing the coupled transportation and power networks with cleaner energy.

This project focuses on the following central question: How to design an efficient and sustainable electrified integrated transit ecosystem that aligns the needs of the individual traveler, and creates values for providers of both transit and on-demand services, and society as a whole?

The DREMTS project explores the concept of demand-responsive electrified multimodal transit systems and develops data-driven network design, and operation control and management methodologies. DREMTS combines high-occupancy electrified vehicles (buses or trains) with electric on-demand mobility services under a mobility-as-a-service framework, and builds new planning and operation algorithms targeting both the mobility operators, the individual user and energy providers.

The project includes 4 threads of research: (1) combined mobility and energy network design; (2) energy and transit cooperative algorithms for shared-mobility fleet operations; (3) prediction of individual behaviour under Mobility-as-a-Service (MaaS) and design of dynamic pricing and incentive mechanisms at the trip and individual level; (3) large-scale agent-based simulation platform to validate the new generation of algorithms.

Together with our team (with a background in machine learning, reinforcement learning, Bayesian modelling and microscopic traffic simulation), the successful cadidate will focus on the research thread (3) and (4) above, namely:

• Extend an open-source large-scale urban mobility simulators (SimMobility) with energy supply simulation and build a realistic large-network case.
• Explore new machine learning methods, namely reinforcement learning, for the design of pricing and incentivive algorithms at the individual and trip level within a MaaS setting.

The successful candidate is also expected to collaborate with the PhD student at KTH that will work on research threads (1) and (2).

DREMTS aims at significant sustainability impacts in the decision making process of the next generation multi-modal public transportation systems. Several MaaS initiatives and public transportation electrification have been initiated around the world and DREMTS will support directly such initiatives in Denmark, Sweden, and beyond.

Responsibilities and tasks

• Conduct a literature review and learning in large-scale mobility and energy simulation frameworks.
• Perform mathematical model formulation and estimation using statistical methods;
• Code implementation and validation for these models within the context of a simulation environment.
• Design, implement and thoroughly test model-based reinforcement learning approaches for pricing and incentives.
• Design, implement and thoroughly test personalization algorithms.
• 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 teaching, teaching support and BSc./MSc. co-supervision as part of the overall PhD contract.

Qualifications

We require the following:

• MSc background on Computer Science, Transportation, Applied Mathematics, Environmental or Electric Engineering and Operations Reseaerch, Industrial Engineering or related is required;
• Excellent background in statistics and probability theory is required.
• Good programming capabilities, in C/C++/C# and at least one scientific language (e.g. Python, Matlab, R, Julia) are required;
• Experience with machine learning is preferred.
• Experience with reinforcement learning and/or traffic simulation is favoured.
• 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 and digital technologies.
• Good communication skills in English, both written and orally;
• Willingness to engage in group-work with a multi-national team;
• Ability to work independently;
• 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.

Assessment

The assessment of the applicants will be made until the position is filled and no later than 15 October 2022.

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 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 in a full-.time postion.

You can read more about career paths at DTU here.

Further information

For more information, please contact Carlos Lima Azevedo climaz@dtu.dk or Filipe Rodrigues rodr@dtu.dk.

You can read more about the Machine Learning for Smart Mobility group at http://mlsm.man.dtu.dk/ and DTU Management at www.man.dtu.dk/english.

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.

Application procedure

Your complete online application must be submitted no later than 15 September 2022 (Danish time). Apply online here: PhD scholarship in Modelling and Simulation of Demand Responsive Electrified Multimodal Transit Systems

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 (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.

MLSM

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
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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