Master Internship in AI for biological microscopy

Created in 2008, the Inria Saclay Center is located at the heart of the Paris-Saclay scientific and technological excellence cluster, which alone accounts for 15% of French research. Serving the development of the Université Paris-Saclay and the Institut Polytechnique de Paris, the Inria Saclay center employs 80 people in research support services and 500 scientists of 54 nationalities.

Benefiting from continuous growth, the center now has a total of 42 project-teams and two in the process of being created, including 21 jointly with the Institut Polytechnique de Paris, 16 with the Université Paris-Saclay, as well as 7 Inria EPs, including one in collaboration with Onera and one with the Pôle Universitaire Centre Val de Loire. These research teams are spread over more than ten sites.

CARS (Coherent Anti-Stokes Raman Scattering) microscopy is an advanced nonlinear optical imaging technique that provides label-free vibrational information about biomedical samples. Broadband laser sources known as “supercontinuum” sources enable the exploration of a wide range of vibrational transitions within the sample. Combined with spectral detection, multiplex CARS (M-CARS) yields hyperspectral images (HM-CARS), in which each pixel contains a 1,024-point spectrum reflecting the chemical composition of the sample.

Using this approach, the project partners https://www.multi-opto-diag.fr/khi-malma-piq/ recently demonstrated that the so-called “silent region” of HM-CARS spectra contains information that can help discriminate between samples. The biomedical potential of this information remains largely unexplored. Current data analysis methods are laborious and face several practical and methodological challenges.

The main objective of this internship is to develop fast, reliable, and automated methods for extracting information relevant to the characterization of muscle diseases from HM-CARS images. The work will combine AI-based approaches with optimization-based methods, notably by formulating spectral unmixing as an inverse problem and developing advanced proximal optimization algorithms to solve it.

• Understand the principles of HM-CARS imaging and the challenges of data processing for muscle disease characterization.
• Explore the dataset and implement suitable preprocessing methods.
• Formulate spectral unmixing as an inverse problem and develop solution methods based on proximal optimization.
• Develop supervised learning approaches to exploit the spectral and spatial information in the images.
• Evaluate and compare the proposed methods in terms of accuracy, robustness, and computational time.
• Write scientific reports and present the results.
• Participate in scientific meetings with the project partners.

• Proficiency in the Python programming language and the PyTorch or TensorFlow environment is required.
• Experience in machine learning / neural networks is strongly recommended.
• Candidates must have validated a course in mathematical optimization.

• Subsidized meals
• Partial reimbursement of public transport costs
• Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
• Possibility of teleworking and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Access to vocational training

Gratification