UMR CNRS 7253

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fr:actualites [2016/09/08 16:22] – [Formation par la RV - Journée du GDR IG-RV le 5 fevrier 2016] ithouvenfr:actualites [2016/10/20 19:16] (Version actuelle) – [Postdoctoral position at CNRS HEUDIASYC 2 years /The deadline for applying is on 1st october, 2106.] ithouven
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-======Postdoctoral position at CNRS  HEUDIASYC  2 years /The deadline for applying is on 1st october, 2106.====== 
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-==Evaluation of Training through an Adaptive Virtual Environment without Prior Knowledge== 
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-Subject: Evaluation of interaction in a virtual informed environment 
-Dates: 24 months, starting before December 15, 2016 
-Location: UMR CNRS 7253 Heudiasyc  
-Sorbonne Universités, Université de technologie de Compiègne  
-Génie Informatique - 57 avenue de Landshut 60203 Compiègne (France) 
-Phone +33 (3) 44 23 45 47   (45mn from Paris by train ) 
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-We are inviting applications for one postdoctoral position on the Evaluation of an Adaptive Training Virtual Environment at CNRS (https://www.hds.utc.fr/). The position is for two years, starting before 15th December 2016. The successful candidate will join the Heudiasyc research team 
-(https://www.hds.utc.fr/).) at the CNRS premises within the Campus of the UTC (http://www.utc.fr/the_university/index.php), in Compiegne, France. The research activity will be carried out under the supervision of Indira Thouvenin (http://www.hds.utc.fr/~ithouven ). 
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-==Description of the mission== 
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-Context 
-Feedback selection is a major issue for intelligent virtual environments for training. Expert knowledge provides useful insight, but it can be difficult to collect. Furthermore, the influence of a specific type of feedback may vary across trainees and over training, which is rarely reflected in expert knowledge. As part of an automatic gesture training system, we modelled the co-evolution between a trainee and a training environment. We consider trainees by their levels in every dimensions of the task, and the training environment as the relevance of every element in its set of feedback. Over time, this co-evolution is reflected by trainee successive performances, and changes in the feedback selection. We model these interactions as a multi-armed bandit problem, each arm representing a type of feedback. This allows to adapt feedback selection only relying on the interactions between the trainee and the training environment, without any prior knowledge. Combining the trainee and the set of feedback in a single representation space, we show how our model can provide useful indicators regarding trainees progression and feedback relevance. 
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- Objective 
-The goal of this post-doc is to evaluate the impact of adaptation in training virtual environments. We believe that co-evolution based on the enaction theory has a positive impact on training. The candidate will design an experiment in order to validate the model. Two platforms are available to conduct this experiment: a 2D platform (PhD Remy Frenoy) and a 3D platform (extension of the model in a CAVE). One major issue is the role of feedback for controlling the interaction. A second issue is the impact of giving an importance to errors in training, in order to propose a flexible, personalized and easy of use training. 
-Requirements for this position include: 
-- a PhD in psychology, cognitive sciences, neurosciences or human factors for CHI 
-- a strong background as an experiment-planner, organizer and performer 
-- strong knowledge and experience for performance evaluation and data analysis  
-- a good publication record in related conferences and journals 
-- fluency in written and spoken English, spoken French will be considered 
-- good relational and oratory skills 
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-==Applications are to be sent to both indira.thouvenin@utc.fr and service.rh-recrutement@utc.fr== 
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-• one unique file or a file archive with a short cover letter indicating the candidate's research interests, achievements to date and vision for the future, a CV including a complete list of publications, the contact details of two/three referees 
-• objet: [recrutement post doc COOL] 
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