EnTimeMent aims at a radical change in scientific research and enabling technologies for human movement qualitative analysis, entrainment and prediction, based on a novel neuro-cognitive approach of the multiple, mutually interactive time scales characterizing human behaviour. Our approach will afford the development of computational models for the automated detection, measurement, and prediction of movement qualities from behavioural signals, based on multi-layer parallel processes at non-linearly stratified temporal dimensions, and will radically transform technology for human movement analysis.
In this context, we are looking for a highly-motivated scientist who will design and implement complex multi-agent motion capture set-ups (e.g. dance or orchestra/quartet scenario) fitting the EnTimeMent approach. The successful candidate will also lead the data analyses (e.g. Granger’s causality or Kuramoto’s model), draft manuscripts and grant proposals.
We are looking for an inquisitive mind with the curiosity to design, collect and analyze large scale motion capture databases, requiring a rethinking of how to extract meaningful group-level statistics: