Doctoral researcher in Dynamic stochastic learning of train dynamics as enabler to highly automated train operation

vor 3 Wochen


Zürich, Schweiz ETH Vollzeit

Job description

We expect the following tasks to be performed:

Analyse understand and categorize data available Determine multiple approaches for calibrations of train motion models based on different model specifications and data available. We expect such model to be embracing the uncertainty and variability described in the data by means of uncertainty aware calibration and Bayesian estimation Include how experts currently operate and acquire feelings for the machine they are driving, also based on a hybrid machine learning approach, integrating rule-based systems with data-driven models

The ultimate objective is to be both precise /accurate, parsimonious, and useful for decision making. Moreover there is a lot of expert knowledge to be considered at multiple levels, both as input of the model specification, and as output of the model estimation. The process, data and output should determine standards and specification for both human driver and automated train operations, and trajectory planning for any downstream optimization of the schedule, for both passenger trains, and especially for freight trains (which have very variable composition, weight, length).

Your profile

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

Your research track is consistent and shows a track record, or clear potential, for modelling, control, and optimisation of transport systems. You are highly motivated and self-driven, with a clear research vision, academic ambition, and excellent communication and writing skills (fluent spoken and written English is mandatory). Moreover, the following skills are expected of a promising candidate:

Ability to program independently complex software Background or exposure to data science / machine learning / AI approaches and software tools Knowledge of statistical modelling of processes 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.


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