M/F PhD offer on Spike Sorting algorithm implementation on Hybrid FPGA/ASIC platform for next generation of Brain Computer interfaces

Job Description

Thesis Subject

Context
Emerging brain–computer interface (BCI) technologies aim to help individuals with disabilities recover motor or communication abilities. A key challenge in advancing these systems lies in their computational demands: current BCIs generate massive data streams, require substantial energy consumption, and often depend on centralized computing architectures that limit real-time performance and portability.
To address these limitations, we propose a novel approach based on neuromorphic computing, an artificial intelligence paradigm inspired by the brain's architecture and dynamics. Unlike conventional digital systems, neuromorphic platforms use energy-efficient hardware and event driven algorithms that mimic neural processing. This enables fast, low power analysis of neural signals directly on small embedded devices — a capability strongly aligned with next generation BCI applications.
Project Description
We have developed a neuromorphic spike-sorting pipeline [1] based on Locally Competitive Algorithms (LCA), referred to as NSS (Neuromorphic Spike Sorter). NSS has been validated in simulation and has demonstrated promising performance on neuromorphic hardware such as Loihi 2.
The goal of this PhD project is to enhance the capabilities of NSS by implementing it on hybrid analog–digital neuromorphic hardware. More specifically, we aim to deploy NSS on the neuromorphic platform developed at 3IT, which combines:
• Digital FPGA-based architectures offering configurability and adaptability
• Analog CMOS/memristor circuits providing exceptional energy efficiency and low-latency processing
This hybrid approach has the potential to significantly advance embedded neuromorphic processing for real-time BCI applications.
Tasks of the PhD Candidate
The PhD candidate will be responsible for designing innovative methodologies to align neuromorphic algorithms with the physical constraints of the target hardware. This hardware–software co design effort will involve:
• Deepening and extending NSS-related machine learning and neuromorphic algorithms
• Adapting algorithms to mixed-signal and analog hardware constraints
• Developing and testing implementations on FPGA and analog neuromorphic circuits
Conducted as a cotutelle between the University of Lille and the University of Sherbrooke, the project provides a unique interdisciplinary environment. The student will collaborate closely with:
• LilleAndCog neuroscience center (Lille, France) — spike sorting and neural signal processing
• 3IT / LN2 (Sherbrooke, Canada) — neuromorphic hardware and embedded AI
The candidate will also participate in the ANR Gneuro initiative, interacting with teams working on:
• Biological neuronal cultures (UGA – Grenoble)
• Microelectrode fabrication for electrophysiology (IEMN – Lille)
• Biosignal analysis and neuromorphic modeling (LilleAndCog - Lille / 3IT LN2 - Sherbrooke)
Candidate Profile
The ideal candidate will have:
• A strong background in electrical engineering, computer engineering, or a related field
• Advanced programming and hardware testing skills in both analog and digital domains
• Experience in machine learning, neuromorphic computing, or embedded AI (asset)
• Knowledge of biotechnology or neuroscience (asset)
• Excellent communication, autonomy, and teamwork skills, essential for managing a cotutelle program

Your Work Environment

This PhD project will be carried out within the framework of an international cotutelle between the University of Lille (France) and the University of Sherbrooke (Canada), providing the candidate with a multidisciplinary research environment at the interface of neuroscience, neuromorphic computing, and embedded electronics.

The project builds upon the complementary expertise of four internationally recognized research institutions. The IEMN (Institute of Electronics, Microelectronics and Nanotechnology, CNRS UMR 8520, Lille) will contribute its expertise in the design and fabrication of microelectrodes and devices for interfacing with biological systems. In particular, IEMN develops advanced technologies for electrophysiological recording and next-generation brain–computer interfaces.

The Lille Neuroscience & Cognition Center (LilNCog, University of Lille, Inserm, Lille University Hospital) is a leading research center in integrative and computational neuroscience. Its expertise in neural signal processing, electrophysiological data analysis, and spike-sorting algorithm development represents a key component of the project. The Neuromorphic Spike Sorter (NSS) algorithm was notably initiated and developed within this scientific environment.

In Canada, the project will benefit from the expertise and facilities of the Interdisciplinary Institute for Technological Innovation (3IT) at the University of Sherbrooke, a world-class research center dedicated to advanced microelectronics, photonics, embedded artificial intelligence, and intelligent systems. The 3IT offers unique infrastructures for the development and characterization of low-power analog, digital, and hybrid electronic circuits.

The PhD candidate will also be integrated into the LN2 (Laboratory for Nanotechnologies and Nanosystems – CNRS IRL 3463), an international laboratory jointly operated by CNRS and the University of Sherbrooke. LN2 provides a unique environment for research at the intersection of nanotechnology, advanced microelectronics, and hardware-based artificial intelligence. It promotes strong Franco-Canadian collaborations and offers an ideal framework for hardware–algorithm co-design projects.

The scientific organization of the PhD project will rely on close interactions between these different institutions. Research activities related to neuromorphic spike sorting, unsupervised learning algorithms, and neural signal analysis will be primarily conducted in collaboration with LilNCog and the University of Lille. Hardware implementation, adaptation of algorithms to analog and mixed-signal constraints, and deployment on hybrid neuromorphic platforms will be carried out at 3IT and LN2 under the supervision of experts in neuromorphic architectures.

Through the cotutelle framework, the PhD candidate will spend extended periods in both France and Canada, ensuring full integration within the French and Canadian research teams. This organization will enable a genuine co-construction of the research activities, from algorithm design to experimental validation on neuromorphic hardware, while providing the candidate with an outstanding international training experience and a dual PhD degree awarded by both the University of Lille and the University of Sherbrooke.

Constraints and risks

Not applicable

Compensation and benefits

Compensation

2300 € gross monthly

Annual leave and RTT

44 jours

Remote Working practice and compensation

Pratique et indemnisation du TT

Transport

Prise en charge à 75% du coût et forfait mobilité durable jusqu’à 300€

About the offer

About the CNRS

The CNRS is a major player in fundamental research on a global scale. The CNRS is the only French organization active in all scientific fields. Its unique position as a multi-specialist allows it to bring together different disciplines to address the most important challenges of the contemporary world, in connection with the actors of change.

CNRS

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