Studierende finden an der ETH Zürich ein Umfeld, das eigenständiges Denken fördert, Forschende ein Klima, das zu Spitzenleistungen inspiriert.
The Institute for Transport Planning and Systems at the Department of Civil, Environmental and Geomatic Engineering at ETH Zurich invites applications for a post doc in “probabilistic state estimation, and prediction models for railway operations”.
Despite the excellent quality of railway systems in Switzerland, railway systems needs to increase their capacity, to match the ambitious targets from policy and environmental goals. Punctuality, travel time, and customer satisfaction should be kept at the same level, or even increased, to remain attractive, under increasing constraints. Railway Traffic Management Systems aim at managing uncertainty in real time, by adapting a pre-defined plan of railway operations to ever changing situations, reducing delays, improving performance.
This project, funded by the Swiss national research fund, is to design the entire chain of current tools in railway traffic management, when large quantities of data are available, and the inclusion of uncertainty becomes explicit. This refers to a probabilistic problem of state estimation (i.e. positioning of trains), probabilistic prediction of train movement and future operations (i.e. prediction of delays), as well as stochastic optimization of train operations under uncertain future events (stochastic and robust optimization). Moreover, this topic has to include constraints on mathematical optimization, ICT, computer science, process management, which currently practical implementation of uncertainty-aware methods in railway traffic management systems. We envisage for tackling those challenges a new approach including explicitly uncertainty as estimated by data or simulation approaches. The specific position is envisaged to result in probabilistic state estimation, and prediction models for railway operations in a short term future, which exploit advanced approaches, stochastic modelling, artificial intelligence/ analytics tools. This will fit into the already existing research team of 3 other researchers at doctoral and post doctoral level. We expect regular interaction with research groups at railway companies in Switzerland and elsewhere, and at relevant research groups at international level.
You ideally have a Doctoral Degree in transport sciences, management/ decision sciences, econometrics, statistics, computer science or related fields. Your research track is consistent and shows a track record, or clear potential, for application of stochastic prediction in transport systems.
You are highly motivated, self-driven, with a clear research vision and academic ambition, you have excellent communication and writing skills (fluent spoken and written English is mandatory). Moreover, the following skills are expected of a promising candidate:
You enjoy working in an interactive international environment with doctoral students, post-docs and senior scientists, referring continuously to practical problems and solutions. This position will be available as of July 2020 or upon agreement; the planned duration of the initial contract is one year, to be extended based on successful performance, for up to 2 years more.
We look forward to receiving your online application. The selection will be based on a multi-step application process. Firstly, applications (motivation letter describing how the past experience and motivation fits the profile sketched in this call, plus CV with list of publications, diploma and phd copies, and 2 reference letters / contacts of referees) will have to be submitted.
We explicitly encourage female candidates to apply. After a first selection, potential candidates will be contacted for a final selection, which will be based on the candidates’ qualifications as well as on a personal interview with the supervisors. Applications via email or postal services will not be considered.
29-02-2024
Bitte sage uns, wo du ähnliche Stellenanzeigen suchst und vergiss nicht deine E-Mail Adresse anzugeben!