Contexte et atouts du poste

This PhD project will be realized in the Inria NERV team, a research lab supported by the French institutions Inria, Inserm, CNRS, and Sorbonne University. The team is located in the Paris Brain Institute (ICM) within the Pitie-Salpetriere hospital.

The NERV team pursues a multidsciplinary research program at the intersection between biomedical engineering, complex systems and clinical neuroscience. NERV proposes new computational frameworks to analyze and model the spatiotemporal complexity of brain networks from multimodal and longitudinal neuroimaging data, and we design noninvasive intervention strategies based on brain-computer interfaces. Furthermore, the team ejoys a privileged position within a unique scientific and technological environment including comprehensive experimental core facilities (eg, neuroimaging, genetics, cellular), several animal models (eg, from nematodes to humans) and powerful centralized cluster computer system to realize big-data analysis and simulations.

Mission confiée

This PhD project aims to explore the role of higher-order interactions in the development of advanced brain-computer interfaces (BCIs).

Current BCI systems often rely on linear models and first-order relationships to decode brain signals, which often leads to limitations in accuracy and adaptability. This research seeks to move beyond these constraints by investigating how higher-order interactions among neural signals can improve the performance and functionality of BCIs

Principales activités

The first phase will focus on developing a theoretical framework for understanding and quantifying higher-order interactions in neural data. In a second phase, novel machine learning algorithms will be designed to leverage the insights gained from the theoretical framework. This will include implementing advanced techniques such as deep learning architectures that can automatically discover and model these higher-order interactions in real time.

The final phase will involve validating the developed models through experiments with human participants using EEG data. The goal will be to assess improvements in BCI performance, such as more accurate intention decoding, ultimately enhancing user experience and efficacy in applications ranging from assistive technologies to neurofeedback.

Compétences

Required skills

The ideal candidate should have a solid background in experimental physics, machine learning and data analysis, as well as experience in laboratory projects and simulations (Python, MATLAB). The ability and willingness to learn will do equally well.

Avantages

• 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