Machine Learning PhD Programs for Rail Operations – DTU Management

    Machine Learning PhD Programs for Rail Operations - DTU Management

    Machine Learning PhD Programs for Rail Operations – PhD studies with a scholarship at DTU Management, Technical University of Denmark, Denmark


    Deadline to Apply

    May 01, 2021 (23: 59 GMT +1)


    Overview

    PositionPhD Position
    No. of Position(s)One
    Research Area– Computer Science
    – Transport Modelling
    – Applied Statistics
    – Operations Research
    ScholarshipAccording to Standard Norms
    WorkplaceDivision for Transport
    DTU Management
    Technical University of Denmark
    Denmark
    Contract Period3 Years

    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

    Responsibilities

    • 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.

    How to Apply?

    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.

    Documents Required

    Applications must be submitted as one PDF file containing all materials to be given consideration. 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)

    About the SORTEDMOBILITY project

    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.

    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).

    Note

    • The assessment of the applicants will be made until the position is filled and no later than 1 May 2021.
    • All interested candidates irrespective of age, gender, race, disability, religion or ethnic background are encouraged to apply.

    Inquiries

    Carlos Lima Azevedo
    E-mail: climaz@dtu.dk

    Filipe Rodrigues
    E-mail:rodr@dtu.dk 

    Fabrizio Cerreto
    E-mail:fceo@bane.dk

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    PhD student in Machine Learning for Rail Operations

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