Postdoctoral Researcher in Assessing the Realism of Virtual 3D Environments for Learning – SimFi-Ed

Organisation/Company Le Mans Université Department LIUM Research Field Communication sciences Educational sciences Neurosciences Psychological sciences » Psychology Researcher Profile Recognised Researcher (R2) Positions Postdoc Positions Application Deadline 1 Nov 2026 - 23:59 (Europe/Paris) Country France Type of Contract Temporary Job Status Full-time Offer Starting Date 4 Jan 2027 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

Job : Searcher (The standard job description can be consulted at this link , on pages 1738 et seq., under reference FPRCH007. This document details the main tasks associated with the occupation, as well as the skills, interpersonal abilities and special features that may be required. )

Description of the research subject

This position is part of the ANR project SimFi-Ed, which aims to study the neural correlates of distraction in virtual reality, and more specifically the EEG characterisation of attentional mechanisms related to simulation fidelity and the prediction of distractibility from gaze.

Planning research project

The ANR SimFi-Ed project is scheduled to run from November 2026 to May 2030. The proposed contract is scheduled to run from January 4, 2027, to January 3, 2029.

Phase 1: a) Develop and conduct an experimental protocol for EEG data capture invovling participants viewing scenes with visual defects predicted to affect visual attention (7 months). b) Process the data and analyze it to identify attentional mechanisms linked to visual distraction (6 months).

Phase 2: in collaboration with a PhD student in computer science, a) Develop and conduct a similar second experimental protocol combining, this time, EEG and eye-tracking measurements placing participants in realistic virtual reality environments, still presenting visual defects (3 months). b) Data processing and correlating EEG and gaze data with the aim of identifying ERP/gaze links predictive of visual distractions (4 months). c) Development of a "distractibility index" based on data from the previous step (5 months). d) Write a report presenting the results and findings of phase 1 and 2 (3 months).

Assigned activities and expected results

1. Characterise the neural mechanisms of fidelity-related distraction. Design and run an EEG study in VR (50 participants) in which scenarios systematically vary simulation fidelity, e.g., object-placement incongruities and lighting inconsistencies. Participants perform an anomaly-detection task in a block design presenting isolated objects and objects in scenes in succession. Brain activity is recorded with a mobile EEG system compatible with VR headset.

2. Relate neural activity to gaze and build a distractibility index. Run a second study (40 participants) replicating a scene-exploration protocol with simultaneous recording of mobile EEG and eye tracking in VR, in order to carry out fixation- and saccade-locked ERP analyses.

Analyse the relations between oculomotor features and ERP components, for example, using representational similarity analysis (RSA) in order to identify the components that consistently correspond to gaze patterns and to compare defect types with one another. Contribute, with the PhD student, to building a machine-learning model predicting an ERP-coded signal from gaze data.

3. Disseminate the results. Write journal articles (e.g., Journal of Cognitive Neuroscience, Brain and Cognition, Visual Cognition, Journal of Vision) and conference papers (e.g., Society for Neuroscience, Cognitive Neuroscience Society, ECVP).

• experience with mobile EEG or EEG in virtual reality is an asset;
• solid skills in statistics and experimental design;
• skills in machine learning applied to neural signals (EEG decoding, RSA) are an asset;
• experience with eye tracking and/or virtual reality (Unity) is an asset;
• fluent English (written and spoken);
• autonomy, rigour, and a taste for interdisciplinary work.

Specific Requirements

Equipment: mobile EEG system dedicated to the project, VR headsets with integrated eye tracking (Varjo; HTC Vive Focus Vision), dedicated computing workstation.
Budget for participant compensation, open-access publication, and attendance at international conferences.
Multidisciplinary team (computer science, learning analytics, vision science, cognitive neuroscien