About our services

The mission of TRI-DSA is to solve relevant societal challenges. We deliver high-quality research to solve real societal problems to companies, governmental institutions, hospitals. We offer support in advanced statistical methods and research that suits your needs.

How can we work together?

  • Contract research

    We conduct and verify research together with you. We agree whether research is conducted at TU/e or whether a team of statisticians and data scientists will come and collaborate with you. There is also the option of setting up longer-term collaborations (e.g. externally financed PhD’s, contracts for a couple of years).

  • Research consultancy

    Smaller research projects aimed at answering your questions. We also offer secondments contracts (i.e, direct support available at site to support you in all your statistical and data analytics questions)

  • Joint grants

    Write together proposals for funding requests at national (e.g. NWO) and international (e.g. ERC) institutions

Our expertise

We are a large team with a broad spectrum of research interests. Here below you can find a selection of topics we can help you with

  • Explorative data analysis for process improvement
    In industrial setting data can help understand and improve the process. We have experience with explorative and confirmative data analysis for clients in order to find and quantify certain patterns in the process.
  • Design and analysis of clinical trials
    Our team can support you in setting up the data collection (e.g. for clinical studies) that is most suitable for your research question. We have experience with designing clinical trials and one of our research topics focusses on modern and adaptive study design.
  • Spatio-temporal data analysis
    Unrevealing spatio-temporal dynamics in complex datasets is one of the research focuses of TRI-DSA. Such datasets refer to complex sets of data in which multiple units are observed over time (leading to intensive longitudinal data, the temporal component), and their physical location leads to dependencies (the spatial component). The disease counts per European country over time and the wave heights across months in various measurement locations in a costal area are just two examples of this type of data that are very common in practice. Analyzing all data simultaneously is complex and cumbersome, and requires additional methodological research. In the past we have worked on the development of moment estimators for spatio-temporal models, and we have worked to build an interactive atlas for a client. We are currently hiring a PhD candidate within TRI-DSA to work on this topic.
  • Predictive analytics
    By using statistical algorithms and machine learning techniques we can analyze historical data and make predictions about future events or outcomes. For example, we can predict future number of customers based on time series and other relevant information.
  • Machine learning for environmental monitoring
  • Design and analysis of measurement validation studies

Are you searching support on a topic that is not listed in here? Please get in contact with us to check whether we can work together.