PhD student in Machine Learning for Rail Operations

 

DTU Management’s Transport Division and Banedanmark would like to invite applications for a 3-year PhD position starting no later than 1 August 2021.

This position is an Industrial PhD position, funded by Innovation Fund Denmark under the project “SORTEDMOBILITY: Self-Organized Rail Traffic for the Evolution of Decentralized MOBILITY”, JPI Urban Europe.

The successful candidate will be employed by the Capacity development group at Banedanmark, Traffic Division and will be also part of the Machine Learning for Smart Mobility Group at DTU. The work will be carried out under the supervision team composed by Associate Professor Carlos Azevedo (DTU), Associate Professor Filipe Rodrigues (DTU) and Dr. Fabrizio Cerreto (Banedanmark).

Project Background
The larger SORTEDMOBILITY project aims at developing pioneering models and concepts for a new generation of self-organising railways. Inspired by natural systems such as ant-colonies, intelligent trains will negotiate individual scheduling decisions to optimise service levels and demand satisfaction in relation to the multi-modal transport network in urban areas. The aim is to improve flexibility, capacity and resilience of the railway system as a mobility backbone, to accomplish an efficient and demand-aware urban and interurban rail mobility growth. The SORTEDMOBILITY project will be carried out by an international consortium of universities and railway companies from Denmark, France, Italy and the Netherlands.

This specific PhD project will focus on the development of consistent demand prediction models for real-time optimization of the self-organising rail system. More specifically, different model-based machine learning models to predict origin-destination matrices and within-rail system route choices will be proposed, developed and tested for integration in online self-organising optimization frameworks. Historical and simulated data will be used for training and testing of the different probabilistic multi-output architectures that will account for contextual information (e.g., time of day, day of week, special events), and provide for a proper treatment of uncertainty. The model will be interfaced with the algorithms for self-organizing operations and refined for online application.

Overall, this research lies in the intersection between Machine Learning, Optimization and Behaviour Modelling. This is a unique opportunity to build your research profile under a collaborative large network sustained by a European-funded project.

We are looking for excellent applicants with MSc background either on Machine Learning, Transportation, Behaviour Modelling, Applied Statistics or related.

Responsibilities and tasks

• Develop and evaluate machine learning models for real-time prediction of demand in current and future rail systems.
• Participate in the development disaggregate (individual) demand prediction models for rail users.
• Develop interfaces for the integration of prediction models in real-time self-organizing rail frameworks.
• Integrate the developed methods and knowledge in Banedanmark’s operational environment.
• Collaborate with researchers from operations research, computer science and transportation simulation in a truly interdisciplinary environment.
• Co-author scientific papers aimed at high-impact journals.
• Participate in international conferences.
• Participate advanced classes to improve academic skills
• Carry out work in the area of dissemination and teaching as part of the overall PhD education.

Qualifications 

• A MSc degree in Computer Science, Transport Modelling, Applied Statistics, Operations Research or similar
• Excellent background in statistics and probability theory is required.
• Previous experience with Machine Learning is highly favored.
• Good programming capabilities in at least one scientific language is required.

The following soft skills are also important:

• Curiosity and interest about current and future mobility challenges (e.g.: automation).
• Good communication skills in English, both written and orally.
• Experience in writing and publishing scientific papers is an advantage.
• 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.

Assessment
The assessment of the applicants will be made until the position is filled and no later than 1 May 2021.

PhD education at DTU
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 research and education opportunity in an international environment. We strive for academic excellence in an environment characterized by collegial respect and academic freedom tempered by responsibility.

Salary
You will be employed in relation to the current labour agreement and your salary is settled based on your experiences and qualifications. The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations.

Contact
If you want to know more about the position, please contact Carlos Lima Azevedo, climaz@dtu.dk, Filipe Rodrigues, rodr@dtu.dk or Fabrizio Cerreto, fceo@bane.dk. 

Application
Your complete online application must be submitted no later than 1 May 2021 (Danish 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)

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

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

Apply