[~Jun 8] PhD position on Personalised Game Experiences
Posted: 24 May 2017 06:59 PM
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Hanna Schraffenberger, Media Tech alumnus, sent me this interesting PhD position at her university (Tilburg)

from https://www.academictransfer.com/employer/UVT/vacancy/40298/lang/en/

PhD position on Personalised Game Experiences in the Data2Game project

Deadline in 16 days

The research group Cognitive Science & Artificial Intelligence of the Department of Communication and Information sciences of Tilburg University is looking for a PhD candidate to join the new project Data2Game, funded by The Netherlands Organization for Scientific Research (NWO). The Data2Game project investigates how, and to what extent, the efficacy of computerised training games can be enhanced by tailoring the training scenarios to the individual player. The research is aimed at achieving three concrete research innovations, corresponding to three subprojects: (1) techniques for the automated generation of in-game narratives that are tailored to the individual player, (2) techniques for the automated modelling of players’ cognitive and affective states, based on in-game data and exhibited social signals, and (3) validated studies on the relation of the player behaviour and game properties to learning performance. The research outcomes will be implemented and validated in a training game for a regional fire department (Brandweer Twente) that will be developed in close collaboration with the company Thales/T-Xchange.

The Data2Game project is carried out in collaboration between the University of Twente, Tilburg University, Thales/T-Xchange and Brandweer Twente.
The current PhD vacancy concerns subproject #2 on multi-modal player modelling. Within this subproject, the PhD candidate will be working on techniques for the automated modelling of players’ characteristics based on in-game data and exhibited social signals, such that training scenarios can be tailored to the individual player. The PhD candidate will particularly investigate (i) to what extent reliable player models can be built based on multi-modal measurements, (ii) to what extent the similarity of game situations can be expressed - in generalized terms - using automated labeling techniques, in order to predict adaptation effectiveness, and (iii) to what extent effective online personalisation of a serious training game can be realized.

See https://www.academictransfer.com/employer/UVT/vacancy/40298/lang/en/ for full text

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