Geometric deep learning applied to medical applications
The PhD subject deals with geometric deep learning and its use in several medical applications.
The merging of these two domains (geometry and artificial intelligence) is at the core of the phD with the conception of SPDnet neural networks that combine both end-to-end training of frequency and spatial parameters with mathematical operations on the variety of symmetric definite-positive (SPD) matrices.
The design of such methods both from a mathematical and software point of view are part of the phD’s objectives as well as their application on public medical datasets like in electroencephalography-based brain-computer interface (BCI).
The expected results consist first in demonstrating the superiority of these geometric approaches over state-of-the-art methods used in BCI and second to identify the best architectures in different medical applications ranging from multi-array data to medical image processing.
Design of a new light-sheet microscope for the temporal monitoring of organoids-on-chip
The subject of the thesis is the development of a fluorescence light-sheet microscope for the optical characterization of organoids-on-chips and 3D organoids. The thesis will focus on the conception of a compact multi-color 3D system, to allow time-lapse imaging of multi-scattering 3D samples directly in a cell culture incubator. The work will begin with a clear understanding of the miniaturization process on the quality of the images. The excitation will be correctly model to avoid optical artefacts and to allow the deepest penetration into the biological tissue. The candidate will be responsible to test different optical strategies as well as different excitation wavelengths. As a final step, the system will be characterized in a cell culture incubator for the morphological and functional monitoring of organs-on-a-chip and 3D organoids using specific fluorescent markers. If needed, novel modifications of the microfluidic chamber with integrated optical functions will be proposed. The research program will mainly focus on the morphological and functional monitoring of two samples: pancreatic organoids on a microfluidic chip and 3D brain organoids.
Multi-target capture strategy for micro total analysis systems
The concentration of biomarkers and pathogens in biological samples is generally limited by the preparation of these samples after their collection. In addition, their detection, when based on an antibody-antigen capture reaction, can be difficult to optimize within biosensors. If the approach which consists of functionalizing a wall to capture molecules or particles flowing in a micro channel seems simple at first glance, the results are often below expectations. On the one hand, the capture of molecules is a convection-diffusion problem; on the other hand, capturing particles must also take into account the pressure distributions on them. Thus the proposed thesis subject is part of a project to optimize the capture and concentration of all types of biological and biochemical targets within fluidic microsystems.
The thesis project will begin by the exploration of models dedicated to the capture of biochemical and biological targets within a microchannel. The objective of this task is to specify the optimal and common conditions for capturing all targets of interest. Among all possible configurations, maintaining functionalized beads dispersed in volume by an adequate field will be favored because it is expected to be optimal. This configuration will be a subject of particular attention, especially as it offers an original microfluidic implementation, particularly in the study of organoids on chips to capture, concentrate and monitor their secretions.
For this project, the laboratory is looking for a student motivated by experimental work in microfluidics with a detailed understanding of the involved physical phenomena. In addition, knowledge of classic molecular biology tests will be appreciated. Skills in numerical simulation are also an asset when applying for the proposed thesis.