<html> <head> <meta http-equiv="Content-Type" content="text/html; charset=utf-8"> </head> <body text="#000000" bgcolor="#FFFFFF"> Two-year Post-doctoral Position <div style="text-align:center"> </div> <div style="text-align:center"> Multimodal data analysis of behavioral and physiological signals from human-human and human-machine interactions </div> <div style="text-align:center"> deadline for application : 30 October </div> <div style="text-align:center"> Laboratoire d’Informatique et des Systèmes (LIS) et Laboratoire Parole et Langage (LPL) <del datetime="2018-03-09T13:30"></del> Aix-Marseille Université & CNRS</div> <div> </div> Keywords: conversational speech, multimodal data analysis, neurophysiological data, machine learning The A*MIDEX project PhysSocial aims at a better understanding of the specificities of social interactions by comparing relationships between behavior and neurophysiology in human‐human and human‐robot discussion. The goal of the post-doc is to<ins datetime="2018-03-03T10:08"> </ins>analyze the multimodal signals (speech, eyes direction, physiological, and neurophysiologic signals) from conversational activity using signal processing and machine learning methodologies in order to compare the human-human and human-robot interactions. The Post-doc is organized around 2 main tasks: <ul> <li> <span style="font-style:normal;font-weight:normal;font-size:7pt;line-height:normal;font-size-adjust:none;font-stretch:normal;font-feature-settings:normal;font-kerning:auto;font-synthesis:weight style;font-variant:normal">Multimodal data preprocessing: in a first step, the objective is to process the row data (speech, transcribed speech, eyes tracking, physiological and neurophysiological signals) corresponding to human-human and human-robot conversation in order to extract time series corresponding to behavioral features, as well as cognitive events derived from local activity in well-defined brain areas involved in language and social cognition</li> <li><span style="font-style:normal;font-weight:normal;font-size:7pt;line-height:normal;font-size-adjust:none;font-stretch:normal;font-feature-settings:normal;font-kerning:auto;font-synthesis:weight style;font-variant:normal">Machine learning of causal relations: in a second step, time series will be used by statistical learning to identify causal relations between behavioral and physiological features and cognitive events extracted from neurophysiological recording with fMRI. From a learning point of view, one challenge in this project is the high-dimensional data. We address this issue with a focus on the features representation and selection problems.</li> </ul> The candidate should have a Phd in Computer Science, Applied Mathematics, Signal or Natural Language Processing (with solid background in machine learning). The candidate should have a strong background in machine learning and signal processing with a focus on multimodality. Some complementary previous experience would be appreciated in the following topics: • Multimodal data processing • Data science applied to language data The post-doc is fully funded during 2 years as part of the A*MIDEX interdisciplinary project PhysSocial, including personalized training, travel expenses, and conferences attendance. French language is not required. Aix Marseille University (<a href="http://www.univ-amu.fr/en" target="_blank">http://www.univ-amu.fr/en</a>), the largest French University, is ideally located on the Mediterranean coast, and only 1h30 away from the Alps. The application files consists of the following documents: - A detailed curriculum with publications, - A description of Phd subject, - A description of the academic background and copy of academic records and most recent diploma, - 2 recommendation letters (including one from the Phd supervisor) The application files should be sent to : Laurent Prévot: <a href="mailto:laurent.prevot@univ-amu.fr" target="_blank">laurent.prevot@univ-amu.fr</a> and Magalie Ochs: <a href="mailto:magalie.ochs@lis-lab.fr" target="_blank">magalie.ochs@lis-lab.fr</a> For any question, contact : Laurent Prévot: <a href="mailto:laurent.prevot@univ-amu.fr" target="_blank">laurent.prevot@univ-amu.fr</a> <a href="http://www.lpl-aix.fr/person/*prevot*" target="_blank">www.lpl-aix.fr/person/*prevot*</a> Magalie Ochs: <a href="mailto:magalie.ochs@lis-lab.fr" target="_blank">magalie.ochs@lis-lab.fr</a> <a href="http://www.lsis.org/ochsm/" target="_blank">http://www.lsis.org/ochsm/</a> </body> </html>