This work is part of the development of brain-machine interfaces dedicated to restoring mobility for patients with severe chronic motor disabilities. The proposed technological solutions are based on decoding brain signals acquired at the motor cortex level in order to extract movement intentions. These intentions serve as commands for motor compensation systems. Our team is a pioneer in this field, having developed WIMAGINE, one of the first chronic wireless implants, as well as a decoder and effectors adapted to the needs of paraplegic or quadriplegic patients (Benabid et al, The Lancet Neurology, 2019 ; Lorach et al, Nature 2023).
The proposed research follows on from an initial thesis whose objective was to design an integrated circuit capable of replicating the performance of the brain signal decoder with extremely low energy consumption, using a fixed model. However, due to changes in the user's strategy or the natural evolution of their brain structures, the performance of the decoding model tends to deteriorate over time, requiring regular recalibration. Initial strategies to compensate for these phenomena have been identified. The candidate's objective will be to refine these strategies and propose an implementation in the form of a low-power digital circuit.
The thesis will be carried out in Grenoble, within a dynamic project team composed of recognized experts in the design and clinical validation of brain-machine interfaces. The team is particularly distinguished in the design of specific integrated circuits and the development of signal decoding algorithms. This framework will allow the doctoral student to evolve in a stimulating scientific environment and to promote their research work, both in France and abroad.

Phase contrast microscopy and fluorescence microscopy are the two pillars of modern biological imaging. Phase contrast reveals the morphology of the sample, while fluorescent labeling provides specificity to the process of interest. In both cases, the image is the average value of the measured signal. In this thesis, we propose to focus not on the average value, but on the fluctuations observed in phase contrast. This new contrast will be called Fluctuations Imaging. The fluctuations arise from the active and passive transport phenomena that characterize cellular machinery, and it can be assumed that the level of fluctuations is correlated with cellular activity. The objective of the thesis is to detect phase contrast fluctuations, quantify them, and link them to a process of interest using machine learning methods. The object of study will be lymphocyte activation, which is a critical parameter for monitoring rejection in certain patients with type 1 diabetes who have undergone islet transplantation. Fluctuations Imaging would enable tracking without labeling, simplifying the monitoring protocol. The expected work is (i) optimizing a phase contrast microscope to detect fluctuations, (ii) analyzing image sequences to quantify them, and (iii) implementing the developed method on various biological models, some of which will be pancreas-on-a-chip organs. This thesis, at the intersection of instrumentation, biophysics, and biology, is intended for a student with a background in optics, physics, or equivalent, with a good knowledge of image processing and a strong interest in applications in biology and health.