Doctoral Researcher in Optimization and Learning - Zurich, Schweiz - ETH Zürich

ETH Zürich
ETH Zürich
Geprüftes Unternehmen
Zurich, Schweiz

vor 4 Wochen

Lena Schneider

Geschrieben von:

Lena Schneider

beBee Recruiter
Beschreibung

Doctoral researcher in optimization and learning for improved railway operations:


100%, Zurich, fixed-term:


Despite the excellent quality of public transport systems in Switzerland, the railway system needs to increase its performance (quality, for instance, travel time) and capacity (amount of services run) and attract more travellers to match the ambitious targets from policy and environmental goals.

One key aspect of traditional railway transport systems is their plan, which is based on predetermined routes, lines, and scheduled times, with little possibility of adjusting to unplanned and unexpected circumstances.

The project focuses on optimal planning and rescheduling of railway operations.


To do so, large-scale optimisation, typically MILP (Mixed Integer Linear Programming) approaches are used, but they scale poorly and are somehow not directly applicable in industrial practice.

To approach those gaps, it is crucial to learn the real-life constraints, and exploiting advanced computational methods (including pre-computation and/or machine learning) to make the solution process faster and consistently optimal.

This position is to address this gap, together with the two largest industrial players in this field in Switzerland.


Project background:


The project is designed in close collaboration with industry players operating as providers of control systems or as railway infrastructure managers.

The project expects interaction with their existing systems, considering their ultimate customers, i.e. the transport operators. Integration in the optimisation team of the industrial partners is envisaged, which can also include part-time work at their site


Job description:


The focus of this project is to understand how to find and generate solutions better and faster for the problem of temporal planning of railways.

This challenge involves timetabling and rescheduling problems, potentially also considering the rerouting of trains on the network. For those problems, standard mathematical models exist. The research needs to

1) extend the understanding of the domain-related objectives, to grasp the complexity of the real-life decisions by human controllers;
2) improve the solution of large-scale scheduling and routing problems by novel algorithmic, pre-computation, and/or machine learning approaches, which are used in decision support systems.

3) to quantitatively understand the usefulness of the suggested decision compared to the actions actually implemented in real life, to tune and improve the solution process.


The ultimate objective is to have a better, faster decision; and precisely understand what "better" means and how it can be mathematically modelled.


Your profile:


Ideally, you have (or are about to receive) a Masters' degree in transport sciences, management/ decision sciences, econometrics, mathematics, statistics, computer science, physics, or related fields.


  • Computer science and ability to program independently complex software
  • Ability to model and work with algorithmic design, existing software, and data provided by industrial partners
  • Knowledge of mathematical optimisation (MILP, IP, LP) and/or control sciences
  • Team working and communication skills
  • Knowledge of German or similar languages is not required but is a plus


You enjoy working in an interactive international environment with doctoral students, post-docs, and senior scientists, referring continuously to practical problems and solutions.


We offer:

ETH Zurich is a family-friendly employer with excellent working conditions. You can look forward to an exciting working environment, cultural diversity and attractive offers and benefits.


We value diversity:

In line with our values, ETH Zurich encourages an inclusive culture.

We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected.

Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish.


Curious? So are we
:

- a motivation letter describing how the experience and motivation fit the profile sketched in this position
- a CV with a list of publications
- diploma copy

  • 1 reference letter


This position will be available, with an ideal starting date between February 2024 and April 2024, or further upon agreement; the planned duration of the initial contract is 18 months, after which an aptitude colloquium will determine the further continuation of the project.

The total doctoral project is expected to be finished in 4 years in total.
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