Type 1 diabetes is a chronic metabolic disease caused by the autoimmune destruction of pancreatic beta cells, which are responsible for insulin secretion, leading to persistent hyperglycemia. There are currently more than 50 million people with the disease worldwide. Pancreatic islet transplantation is a therapy developed by Grenoble University Hospital for the most severe cases, such as britlle diabetes. These transplants significantly improve patients’ quality of life. However, in 40% of cases, patients experience rejection caused by immune T cells within 5 years of the transplant. Monitoring transplanted islets is complex because they are diffusely implanted in the recipient’s liver, making it impossible to monitor their function or adjust immunosuppressive treatment.

CEA, in collaboration with the Grenoble Alpes University Hospital, is developing a biological twin model to monitor the immune response of transplant patients in real time. This organ-on-a-chip technology will enable the evaluation of the interaction between the graft and the patient’s immune cells, as well as graft survival and function, thereby providing a predictive tool to anticipate rejection. To this end, an opto-fluidic platform dedicated to “organ-on-chip” applications is currently under development. In this context, the L4IV lab is responsible for the platform’s imaging module, encompassing both instrumentation and image processing. 3D phase and fluorescence imaging modalities have been integrated into the device. In particular, an innovative technique for 3D reconstruction of the refractive index of complex samples, such as islets-on-a-chip, has been developed for label-free imaging.

You will work closely with a multidisciplinary team composed of experts in biology, microfluidics, and microscopy. Your primary mission will be to develop algorithms for analyzing time-lapse sequences of multimodal images (phase and fluorescence) acquired on the opto-fluidic platform, with the goal of quantifying the immune response and the state of the islets. You will utilize various image processing techniques such as segmentation, detection, tracking, and colocalization. Deep learning may also be employed to correlate refractive index images with specific islets’ states labeled by fluorescence. Finally, you will be required to participate in image acquisition campaigns.

• Engineering degree and/or Ph.D. in image processing/data science, with a strong interest in biomedical imaging.
• Solid experience in image analysis using conventional methods and/or deep learning.
• Proficiency in Python programming
• Successful experience in a bioimaging group is a plus.
• Fluency in French and/or English (spoken and written), with strong clear and effective communication skills.