Job offer

INRIA Saclay; France; Sur place; EUR 30 000 - 50 000; Plein temps

Résumé du poste

An established industry player is seeking a motivated PhD candidate to join a groundbreaking project aimed at developing medical optics solutions for myopia treatment. This role involves leveraging advanced Bayesian methods to calibrate a numerical eye model, addressing challenges in optimization and uncertainty management. The multidisciplinary team collaborates with leading partners in healthcare and research, focusing on innovative solutions to a pressing public health issue. Candidates will engage in cutting-edge research, utilizing machine learning techniques to create virtual representations of patients' eyes, contributing significantly to the field of visual health.

Qualifications

• Master's degree in engineering, applied mathematics or related discipline required.
• Proven research talent and excellent command of English preferred.

Responsabilités

• Develop Bayesian inference methods to calibrate numerical eye model parameters.
• Create a virtual twin of a patient's eye using machine learning techniques.

Connaissances

Machine Learning

Uncertainty Quantification

Optimization

Academic Writing

Presentation Skills

Formation

Master's degree in Engineering

Master's degree in Applied Mathematics

Description du poste

Organisation/Company: INRIA Saclay

Research Field: Mathematics Engineering

Researcher Profile: Recognised Researcher (R2), Leading Researcher (R4), First Stage Researcher (R1), Established Researcher (R3)

Country: France

Application Deadline: 29 Sep 2025 - 22:00 (UTC)

Type of Contract: Temporary

Job Status: Full-time

Offer Starting Date: 1 Sep 2025

Is the job funded through the EU Research Framework Programme? Not funded by a EU programme

Is the Job related to staff position within a Research Infrastructure? No

Offer Description

The PREMYOM project (Management and Slowing Down of the Myopia Epidemic through Medical Optics) aims to develop medical optics-based solutions to curb the global myopia epidemic. It aims to establish a therapeutic reference for personalized myopia treatment, leveraging unique expertise and a rigorous research, development, and innovation approach.

PREMYOM is a multidisciplinary consortium of renowned industry, healthcare, and research partners coordinated by EssilorLuxottica. It brings together an unprecedented blend of technical, clinical, and digital expertise, including the Hôpital Fondation Adolphe de Rothschild, Inria, InSimo, Institut Mines-Télécom, and the Institut de la Vision.

The French Prime Minister's General Secretariat for Investment (SGPI) and its operational agency, Bpifrance, co-financed the project under the France 2030 plan and the i-Demo-2 public funding program. This selection highlights the critical importance of children's visual health as a major public health issue and the fight against the myopia epidemic in Europe.

A numerical model of the eye is being developed to integrate various geometrical and optical parameters specific to each patient. The goal is to determine the optimal values of these parameters from laser measurements of retinal images reduced to observation vectors.

The current optimization approach to estimate the model parameters faces several challenges, including the lack of guaranteed optimum uniqueness, the existence of local minima, and poor conditioning of the problem. The project aims to develop advanced Bayesian methods for calibrating the eye model and integrating prior knowledge (based on population statistics), rigorous treatment of measurement noise and modeling errors. Developing efficient numerical strategies will give access to the parameters' full posterior distribution, enabling uncertainty and robustness analyses of the model predictions.

The Platon project-team focuses on developing innovative methods and algorithms for uncertainty management in numerical models, including advanced calibration strategies from data (observations, measurements, other model predictions) and uncertainty reduction.

This thesis will primarily develop and implement Bayesian inference methods to calibrate the parameters of the numerical eye model. Special attention will be given to the design of likelihood functions, including the representation of optical aberrations on polynomial bases, such as Zernike polynomials, to ease the comparison between the measurements and the model predictions. An essential aspect of this first part of the work will also concern the reduction of the computational burden of Bayesian methods, using low-cost surrogate models fitted to the calibration task. The PhD candidate will test the proposed methods using synthetic data generated using a model with known parameter values. The thesis will then progressively address model uncertainty by accounting for model errors in the calibration procedure and propose model selection procedures. Finally, the research will concentrate on creating a virtual twin of a patient's eye using laser images through machine learning techniques.

Candidates are required to have a Master's degree in engineering, applied mathematics or a related discipline, and a specialization in machine learning, uncertainty quantification, optimization or related fields. Preferable qualifications for candidates include proven research talent, an excellent command of English, and good academic writing and presentation skills. Applicants should submit a CV, a covering letter as a single document detailing the knowledge, skills and experience you think make you the right candidate for the job, a list of your MSc courses and grades, a copy of your Master's thesis, a list of names of references, and preferably a list of publications. For further details and applications, please contact Pietro Marco Congedo ([e-mail]). All applications should be emailed to [e-mail].