On-line monitoring of bioproduction processes using 3D holographic imaging
Develop a 3D lens-free holographic imaging system coupled with deep learning for real-time monitoring of adherent cell cultures.
Best for:Best for graduate researchers with a background in optics, imaging, or biomedical engineering interested in deep learning and cell monitoring.
Overview
This PhD position at Universite Grenoble Alpes focuses on creating an innovative 3D lens-free holographic imaging microscope for non-invasive, real-time monitoring of adherent cells and complex multicellular systems like organoids. Hosted at the Laboratory for Optical Instrumentation and Information Processing for In Vitro Imaging, the project integrates advanced data reconstruction algorithms and deep learning techniques for single-cell segmentation and dynamics tracking. The successful candidate will work closely with interdisciplinary teams to test the system on various cell models, contributing to advancements in drug manufacturing, regenerative medicine, and organ-on-chip applications. Applicants are expected to hold a relevant master's degree and demonstrate strong commitment to independent experimental research and algorithm development.
What you'll do
- Design and develop a 3D lens-free holographic imaging system for near real-time monitoring of adherent cells.
- Implement and optimize advanced algorithms for optical data reconstruction and analysis.
- Apply deep learning techniques for real-time image segmentation and tracking of single-cell dynamics.
- Test and validate the imaging setup using diverse cellular models and organoid systems.
- Collaborate with interdisciplinary researchers across optics, biology, and data science domains.
Requirements
Degree required: Master of Science or equivalent degree in Physics, Optics, Biomedical Engineering, Computer Science, or related fields
Degree offered: PhD
Language: English or French proficiency as per institutional standards
Instruction: English
- Strong background in optical imaging or microscopy systems
- Experience with image processing and computational data reconstruction
- Proficiency in deep learning and machine learning frameworks
- Programming skills in languages such as Python or MATLAB
- Interest in biological applications and cell culture analysis
- Open to international and domestic applicants holding a qualifying Master's degree
Funding & terms
How to apply
Deadline: 2026-12-31
Submit your application through the official doctoral portal before the stated deadline. Ensure all required academic transcripts and supporting identification documents are uploaded correctly.
- CV
- Cover letter
- BSc + MSc transcripts
- Reference letters
Contact
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