VORtech welcomes interns. Not only to do work that we never have time for ourselves, but also to contribute to the education of a new generation of scientific software engineers.

Below is a list of internships. If there are no internships that match your interests, feel free to contact us anyway to discuss whether we can formulate a new project that is interesting both for you and for us.

We can only accommodate a limited number of internships at any one moment. If you are interested to do an internship at VORtech, call us or send us an email to see whether there is a place available.

Data engineering and machine learning

We are setting up several projects in the field of data engineering and machine learning. If you are interested in this topic, please contact us to hear what we are working on.

Data assimiliation internships

Data assimilation methods incorporate observations into a dynamical computer model of a real system in order to improve the quality of the predictions. Most of the work that we do in this field at VORtech is based on OpenDA. OpenDA is a generic open source software framework for data assimilation and model calibration techniques (

OpenDA is under continuous development. There are always tasks to be done that can be the subject of an internship or MSc project. Contact us to hear what the current topics are.

Below is an example of an actual project with OpenDA for which we are looking for interns.

Data fusion and transport model calibration using OpenDA and OmniTRANS

Problem description

Ever since the emergence of big data, it plays an increasingly important role in both construction (parameter estimation) and application (forecasting) of strategic, tactic and operational transport model systems. In the Netherlands, the numerical models available in OmniTRANS are widely used to build such transport model systems. Although some parameter estimation methods and tools are readily available within OmniTRANS, most models and applications rely on exogenously estimated sets of parameters and/or interfaces with (big) data using custom Ruby scripts. The OpenDA platform contains several data fusion and parameter estimation methods for numerical models and as such could be a more generic solution for construction and application of OmniTRANS based model systems using big data.

Internship assignment

DAT.Mobility is developing a realtime traffic prediction model for operational purposes like traffic management. This realtime traffic prediction model uses OmniTRANS software for estimation of traffic demand, supply and to model the resulting traffic flows, speeds, densities and derivatives thereof. In this methodology many parameters affect the outcome its predictions. The goal of the assignment is to define and construct a test case in which openDA is used to calibrate parameters in OmniTRANS and/or to solve complementary optimization problems in real time traffic predictions. Below, a (non-extensive) list of possible assignments to be answered:

  • Using openDA for parameter estimation for the realtime traffic state prediction model in an online and/or offline environment. Investigation of the parameter estimation methods that suit the stated problem and research on the applicability of these parameter estimation methods in the dynamics of a traffic model environment.
  • Using openDA for matrix estimation in a realtime dynamic traffic model environment. Both traditional (e.g. conventional bilevel solution approaches) as well as more experimental matrix calibration methods (e.g.: fail to satisfy our needs on matrix calibration in dynamic traffic models. Investigation of openDA techniques to be applied in this optimization problem.
  • Using openDA data-assimilation for improved data-fusion of available datasources and/or for other estimation/prediction processes within realtime traffic state prediction.

Research group / information

DAT.mobility/Goudappel Coffeng (Deventer) and VORtech (Delft). Daily supervisors: Leon Suijs MSc (Goudappel Coffeng, DAT Mobility) / Dr. Nils van Velzen (Vortech).

Send your CV and motivation to the address specified above. If there is none, use our contact information.
Contact information