Researcher in AI, images and models for the heart

Mission

The successful candidate will contribute to the development of a digital twin of the heart and blood circulation and will be responsible to design, train and optimise artificial intelligence models capable of predicting the effects of different treatments for mitral insufficiency and ventricular failure with significantly reduced computing time.

Activities

• Collect and process patient-specific data (e.g., imaging, haemodynamics) to build a substantial database

• Become proficient in the use of digital twins

• Design and integrate artificial intelligence methods (neural networks) for predicting and optimising treatments

• Create interactive graphical user interfaces (GUIs) for monitoring and visualising simulated data

• Set up a complete pipeline (simulation, visualisation, interpretation) to facilitate translational research

• Handle contacts with clinicians and manufacturers

• Participate in meetings and promote the results obtained (reports, publications, presentations)

• Disseminate the results obtained through publications in peer-reviewed journals and by participating in international conferences.

Background

The ‘Biological Fluid-Structure Interactions’ (IFSB) team is developing a digital twin of the heart and blood circulation with the objective to improve the management of heart disease, and in particular the treatment of mitral insufficiency and ventricular failure. The digital twin is based on complex equations (equations of continuum mechanics and fluid mechanics) and requires the integration of patient-specific data (heart geometry, haemodynamic measurements, etc.).

While the digital twin represents a high-fidelity model of the patient's reality, it is associated with significant costs in terms of resources and computing time. It is therefore not suitable for routine clinical use.

One strategy to provide clinicians with a tool that is compatible with clinical use and that reduces computational costs is artificial intelligence (AI). It will enable the design of an enriched model while ensuring reasonable computing time.

The research project is funded by the PostGenAI@Paris cluster, which brings together academic partners with diverse expertise, ranging from mathematics and computer science to health, engineering, law, and political science. The consortium benefits from extensive support from industrial and social partners—comprising over 60 institutions, including startups and tech giants. The PostGenAI@Paris cluster addresses fundamental challenges posed by the most recent advances in AI, wherein the interface between computing technology and human intelligence is increasingly blurred. This era of “post-generative AI” offers remarkable benefits to society, but brings with it a host of new uncertainties related to reliability, regulation, and ethics. The PostGenAI@Paris cluster is led by the Sorbonne Cluster for Artificial Intelligence (SCAI). Launched in January 2025, PostGenAI@Paris is a five-year initiative funded under grant ANR-23-IACL-0007.

The research project will be part of the « AI, Images and Models for Medicine » Collaborative Acceleration Program (CAP 2), which is one of the programs developed within the PostGenAI@Paris cluster.

Additional information

Application

CV, cover letter, names of two references

Application dates

From JJ/02/2026 to JJ/03/2026

Type of contract and expected recruitment dates

Fixed-term contract – expected duration of 24 months – to be filled in early April 2026 and run until 31 December 2029 at the latest.

Gross monthly salary

Depending on experience and funding

Hours

1,607 hours per annum

Working environment and context

The successful candidate will join the ‘Biological Fluid-Structure Interactions’ (IFSB) team, one of four research teams at the UTC Biomechanics and Bioengineering Laboratory. The team is specialised in the fields of fluid biomechanics and haemodynamics at the microscopic and macroscopic scales. It focuses on the study of fluid-structure interactions that occur between fluid flows and various flexible structures (vessel walls, cell capsules and membranes, biomedical devices, etc.). The team's strength lies in combining numerical and experimental expertise, which allows it to translate theoretical results into practical applications. The team is internationally recognised for its work on modelling artificial capsules. It works closely with academic, industrial, clinical and transfer partners on patient applications and patent exploitation.

The successful candidate will report to the UTC project managers and maintain regular dialogue with them and all relevant stakeholders.

Occasional travel is to be expected as part of the project.

Qualification: doctorat or PhD

Field: engineering, applied mathematics or computational mechanics.

The successful candidate will need to have the following multidisciplinary skills:

• Proficiency in scientific programming, particularly in C++ and Python

• Expertise in artificial intelligence

• Knowledge of bioengineering/biophysics/haemodynamics is an asset

In addition, they must:

• Be able to manage the project and work well in a team

• Be able to work on multidisciplinary projects involving image processing, numerical simulation, AI analysis and clinical applications.

• Have a very good command of French and English.