Dynamic machine learning of human activities
Static models of human activity are typically inefficient due to their required effort in development and inability to cope with changing concepts during deployment. At the same time, the use of sensors to acquire information in activity recognition systems is often inefficiently handled in an always-on mode. This project will investigate advances in intelligence theories to develop activity recognition and behaviour inference models that can cope with changing concepts. In addition, the fellow will contribute to PhD student coaching and investigates collaborations with industrial partners to leverage the technology.
Interested candidates are invited to submit their application material for one or more positions within the network. In addition, applicants are asked to provide a 1-page statement on their motivation to become part of this exciting network in PDF format by email to [email protected] The statement may be reviewed by all iCareNet partner organisations. Positions will be filled as soon as possible.
- The researcher can be a national of any country.
- The researcher has not lived, worked or studied in the country of the host organisation of this position for more than 12 months in the 3 years immediately prior to the reference date. Short stays, such as holidays, are not taken into account.
This PhD position is one of the 19 prestigious Marie Curie fellowships for PhD and Postdoc positions that are available within iCareNet.