LABDA (Learning Network for Advanced Behavioural Data Analysis) is an EU-funded MSCA Doctoral Network, that brings together leading researchers in advanced movement behaviour data analysis at the intersection of data science, method development, epidemiology, public health, and wearable technology to train a new generation of creative and innovative public health researchers via training-through-research. The main aims of LABDA are to establish novel methods for advanced 24/7 movement behaviour data analysis of sensor-based data, examine the added value of advanced behavioural data analysis and multi-modal data for predicting health risk and facilitate the use and interpretability of the advanced methods for application in science, policy and society. Via training-through-research projects, 13 doctoral fellows will contribute to reaching these aims. Together, they will develop a joint taxonomy to enable interoperability and data harmonisation. Results will be combined in an open-source LABDA toolbox of advanced analysis methods, including a decision tree to guide researchers and other users to the optimal method for their (research) question. The open-source toolbox of advanced analysis methods will lead to optimised, tailored public health recommendations and improved personal wearable feedback concerning 24/7 movement behaviour. For more information, see the project's website:
LABDA projectAbout your roleAs a PhD student in the project 'Temporal data analysis of 24/7 human movement behaviour and value for health' your challenge is to develop methods for temporal analysis of human behaviour amongst others compare temporal analysis techniques in their ability to detect change points in behaviour; explore data-driven techniques to identify subgroups with similar temporal behaviour patterns; and assess complementary, discriminatory and predictive value of temporal data analysis techniques beyond volume-based analyses.
Your tasksYour specific responsibilities will be to:
- Design methods for temporal data analysis of 24-hour movement behaviour;
- Generate and test predictive modelling programs;
- Report on findings by publishing scientific articles, resulting in a dissertation;
- Present findings at (inter)national meetings/conferences;
- Contribute to the LABDA toolbox of advanced analysis methods for sensor-based behavioural data;
- Contribute to educational activities of the department and within the consortium.