Pre-proposals

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 mdimperio2@gmail.com and abdellah.fourtassi@univ-amu.fr

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 (https://www.ilcb.fr/) 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 https://neuralbasesofcommunication.eu/ ) and Prof Thierry Artières (Laboratoire d’Informatique des Systèmes, Marseille https://pageperso.lis-lab.fr/thierry.artieres/ ). 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 (https://ecole-doctorale-62.univ-amu.fr/), embedded in the everyday activity of the two co-supervising teams, as well as in the PhD program of ILCB (https://www.ilcb.fr/education/phd-program/ ).

Please send your Expression of Interest and CV to pascal.belin@univ-amu.fr and thierry.artières@univ-amu.fr before June 1st, 2022

Apply here: applications.ilcb.fr

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

Apply here: applications.ilcb.fr

PhD supervisor : Abdellah 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 (cocodev.fr) 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.

Apply here: applications.ilcb.fr