2022-04849 - PhD Position F/M L∪M_INTERMOD: Cross-cognitive and trans-cognitive modelling of multimodal biomarkers with artificial intelligence methods. Application to the unified language- union-memory framework (L∪M)
Le descriptif de l’offre ci-dessous est en Anglais

Type de contrat : CDD

Niveau de diplôme exigé : Bac + 5 ou équivalent

Fonction : Doctorant

Contexte et atouts du poste

The thesis work will be performed at LPNC UMR CNRS 5105 (https://lpnc.univ-
grenoble-alpes.fr/) under the supervision of Monica Baciu (https://lpnc.univ-grenoble-
alpes.fr/membre/monica-baciu) and a coordinating team composed of Sophie Achard LJK
(https://mistis.inrialpes.fr/people/achard/) and Martial Mermillod LPNC (https://lpnc.univ-grenoble-alpes.fr/membre/martial-mermillod). The PhD student will work in a team including researchers, engineers, post-docs, PhD students and M2R students. He/she will interact with researchers and specialists of the Brain & Cognition program of the UGA IDEX. He/she will be enrolled in the EDISCE doctoral school https://edisce.univ-grenoble-alpes.fr/ in the Cognitive Sciences, Cognitive Psychology and Neurocognition specialty.

Mission confiée

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Principales activités

Scientific Background. The integration of multimodal data to elucidate the neural processes and networks underlying cognition and behavior is a major challenge in cognitive neuroscience. This is associated with a current paradigm shift, with neurocognitive models that consider that complex interactions between cognitive functions enable human behaviors. The fusion (integration) of multimodal data could not only compensate for the limitations of each modality but also detect features that are intrinsically multimodal.

Objectives and research program. This research work is situated at the interface between language and declarative memory and addresses the question of their interactive union in a multimodal and integrative perspective with artificial intelligence methods. The project will have two dimensions, one neurocognitive and another neurocomputational. On the neurocognitive side, the objective is to validate and enrich this new theoretical framework LM language-union-memory that we have recently developed (Roger et al., 2022) and consists in considering that language and
memory are two inseparable functions and that their evaluation must be done in an interactive joint way, rather than in isolation. The project will also have a trans-cognitive dimension to explore this framework in normal and pathological conditions. On the neuro-computational side, we will use several analysis methods (unsupervised machine learning, graph theory, and deep learning) for multimodal fusion and develop transfer learning in the framework of deep learning AI. Multimodal biomarkers (neuropsychology, functional and anatomical neuroimaging) have been acquired in our previous work.


Technical skills and level required : Python

Languages : English



  • Subsidized meals
  • Partial reimbursement of public transport costs
  • Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
  • Possibility of teleworking and flexible organization of working hours
  • Professional equipment available (videoconferencing, loan of computer equipment, etc.)
  • Social, cultural and sports events and activities
  • Access to vocational training
  • Social security coverage