We are looking for an excellent candidate with a master’s degree in MSc in Artificial Intelligence, Computer Science, Mathematics, Statistics, or a closely related field to join a project focused on developing an advanced transparent machine learning framework with application on movement behavioural analysis. Smartwatches and other wearable technologies allow us to continuously collect data on our daily movement behaviour patterns. We would like to understand how machine learning techniques can be used to learn causal effects from time-series data to identify and recommend effective changes in daily activities (i.e., possible behavioural interventions) that are expected to result in concrete health improvements (e.g., improving cardiorespiratory fitness). This research, at the intersection of machine learning and causality, aims to develop algorithms for finding causal relations between behavioural indicators learned from the time series data and associated health-outcomes.