Child development, prosody, gesture, communicative coordination

PhD supervisor : Abdellah Fourtassi and Mariapoala D'Imperio

Labotary : Laboratoire d'Informatique & Systèmes and Laboratoire Parole et Langage

Keywords : child development, prosody, gesture, communicative coordination,

Expected competences of the candidate : experimental design and/or corpus analysis skills.

Summary of the pre-proposal : We are happy to sponsor candidates for application to the ILCB PhD or Postdoc fellowships (deadline by June 12th). We propose a project that investigates children’s mastery of the prosodic structure and function of their native language. We will study how children learn to combine the production of prosodic cues with the use of gesture (especially head movement) in a communicative context (e.g., Esteve-Gibert et al., 2022). The research methods are interdisciplinary and involve laboratory experiments and/or analysis of multimodal corpora. A detailed research proposal will be defined and written together with the selected candidate and the co-advisors Mariapaola D’Imperio and Abdellah Fourtassi.

If interested, please send us your cv at and

You must contact the supervisor you want to work with before your application

The neuronal bases of voice information processing studied using machine learning and primate electrophysiology

PhD supervisor : Pascal Belin, Thierry Artières

Labotary : Institut de Neurosciences de la Timone, Marseille and Laboratoire d’Informatique des Systèmes, Marseille

We invite applications from candidates to a PhD project to be presented in the annual PhD call by the Institute of Language, Communication and the brain ( in Aix-Marseille University, in which 3 PhD grants will be awarded on a competitive basis.

The successful candidate will be co-supervised by Prof Pascal Belin (Institut de Neurosciences de la Timone, Marseille ) and Prof Thierry Artières (Laboratoire d’Informatique des Systèmes, Marseille ). The project aims to measure, via multi-electrode arrays implanted chronically in the auditory cortex of monkeys, the neuronal activity evoked by a large array of complex sounds, and analyze that neuronal activity using machine learning tools, particularly deep-learning, to better understand the neurocognitive architecture underlying cerebral voice processing in primates. This project is part of a larger ERC-funded project entitled “Comparative Studies of Voice Perception in Primates” (COVOPRIM).

The successful candidate will have a Master’s degree or equivalent, strong interest and/or background in machine learning and deep learning, strong interest in Neuroscience and strong motivation to work with primates. He/she will be registered at Ecole Doctorale 62 at Aix-Marseille University (, embedded in the everyday activity of the two co-supervising teams, as well as in the PhD program of ILCB ( ).

Please send your Expression of Interest and CV to and thierry.artiè before June 1st, 2022


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Formal syntactic dependencies syntax and syntax/semantics interface, the structure and computation

PhD supervisor : Viviane Deprez

Labotary : Laboratoire Parole et Langage

Keywords : Syntax, semantics, negation, questions, language deficiencies and acquisition, motor system

Expected competences of the candidate : experimental design, statistical analysis, basic linguistic knowledge

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Building a quantitative theory of child multimodal conversational development

PhD supervisorAbdellah Fourtassi

Labotary : Laboratoire d'Informatique & Systèmes

Keywords : child conversational development, deep learning modeling, unmoderated data collection, cross-cultural comparison, cognitive mechanisms 

Expected competences of the candidate : The project is highly interdisciplinary and we accept applications from researchers with an experimental background to supervise data collection and analysis and researchers with a background in computational modeling (especially in deep learning techniques) to help with modeling child conversational coordination from multimodal data

Summary of the pre-proposal : How do children become able to use language to engage in coordinated conversations? While there is a large body of research investigating children’s acquisition of linguistic structures such as phonology and syntax, in comparison, little is known about how children learn to translate this knowledge into conversational skills such as turn-taking management, negotiating shared understanding with the interlocutor, and the ability for a coherent exchange. This slow scientific progress can be attributed largely to limitations in traditional in-lab research methods typically used to study this question. Our team ( proposes a research approach that goes beyond these limitations. First, we use more ecologically valid designs to collect child-caregiver conversations at home (either via zoom calls or face-to-face) across many cultures. Second, we use deep learning modeling techniques, which allow us to bridge several dimensions of conversational complexity and identify their cognitive mechanisms. This research approach provides a formal framework where we can articulate, contrast, and adjudicate between several hypotheses, answering crucial, lingering scientific questions about the universals of conversational development and its cognitive mechanisms.


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Neurophysiology of Social Interactions with Natural and Artificial Agents

PhD supervisorThierry Chaminade

Labotary : Institut de Neurosciences de la Timone (INT, UMR 7289)

Keywords : Human neurophysiology, Human-Robot interactions, Functional Magentic Resonance Imaging, Natural Conversation, , Artificial Conversational Agents.

Expected competences of the candidate : The candidate should have a borad interest in the field of natural conversational interactions and skills in the use of python tools for the analysis of behavioral and neurophysiological data.

Summary of a pre-proposal : This project provides an unique experimental environment, including a paradigm to record human-human and human-robot conversations in the MRI environment synchronously with brain activity and a number of other behaviours. Participants (n=25 in the corpus) have been scanned using fMRI while they interact with a human or a robot (the conversational head Furhat) through unconstrained language. A cover story hides to participants the real objective of the experiment, namely investigating social interactions. Multiple behaviours are recorded (participant and interlocutor speech, interlocutor head movements fed live to the participant, participant eye movements, brain activity recorded with functional MRI, respiration and peripheral blood flow) and curated to form a synchronized multidimensional corpus. This corpus, shared through various dedicated repository, has since been used to address multiple questions pertaining to the cognitive and (neuro)physiological foundations of natural social interactions, in particular through the comparisons of human-human and human-robot interactions. The project covers the development of new robots' behaviours to improve significantly its social competence, recording of a new corpus of 25 participants interacting with this new conversational robot, preparing the new corpus for analysis according to well-established pipelines and analysis of the multimodal corpus to investigate the physiological correlates of natural human-human conversations compared to the same interactions with a non-human agent.


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