Contexte et atouts du poste

The MIMESIS team is at the forefront of innovation, working in the fields of scientific computing, machine learning, medical imaging, and control. We are an interdisciplinary team, that collaborates closely with clinicians to develop new technologies that can help improve healthcare, in particular through computer-assisted interventions. Our core research activities take place in the biomechanical modeling of soft tissue and developing novel numerical methods for real-time computation. Our research results enable the creation of digital twins of organs for personalized planning, augmented reality during surgery, and control in medical robotics.

The MIMESIS team recently joined the MEDITWIN consortium, whose main objective is to enable doctors to simulate the outcome of various treatment scenarios for a patient. MEDITWIN will enable the clinical validation, and possible industrialization, of these innovations so that these technologies can be deployed in a standardized way, and benefit as many people as possible. The best standards of care will be incorporated into virtualized experiences made accessible worldwide, setting a new benchmark for quality in healthcare and providing a decisive learning ground for progress in medical science. The benefits of digital twins will be assessed for medical teams, patients, and the healthcare system, notably in terms of improving the efficiency of care, quality of multidisciplinary decision-making, and effectiveness and safety of medical practices and interventions. More information about the project and Ph.D. topic can be found here .

Mission confiée

Thermal ablation liver therapy is a minimally invasive procedure for the treatment of certain types of tumors in inoperable liver cancer patients. The aim of this therapy is to destroy tumor cells via the local application of excessively high/low temperatures, which are delivered by percutaneously inserted needle probes. Various approaches for thermal ablation exist, all differing in terms of both their practical use and the results they achieve. Microwave ablation, radiofrequency ablation and cryoablation are the three most commonly used ablation approaches. However, to date, it is impossible to determine a priori either the optimal approach or the optimal delivery configuration.

The aim of the project is to accurately model the physical processes involved in different approaches to thermal ablation, as well as variations in the way they are delivered (energy levels, number of probes, probe placement) and the patient's current cancer status, i.e. to develop a digital twin of liver thermal ablation. This will ultimately allow to predict the outcome of ablation therapy in diverse configurations, which could significantly enhance the planning and execution of the procedures, potentially improving treatment outcomes and reducing complications. This digital twin could also provide valuable insights into the behavior effects of ablation procedures in liver tumors, contributing to the broader understanding of liver cancer treatment.

This ambitious project will require close collaboration with other researchers, engineers and clinicians.

Principales activités

The main steps of the research project are:

1. Digital twin development: determine the set of partial differential equations that best describe the physical processes involved in each of the different approaches to liver thermal ablation therapy. Develop a computational model of the liver that accurately reflects the patient's anatomical and physiological characteristics. Develop computational schemes for the numerical resolution of the resulting initial value problems for thermal ablation therapy simulation.
2. Parametrization: develop AI-based medical image characterization methods for the parametrization of the ablation therapy computational models, taking into account patient-specific tissue and disease characteristics.
3. Ablation procedures outcome prediction: use the parametrized ablation therapy digital twin to simulate the main two main ablation therapy procedures in various delivery configurations. Use the simulation outputs to predict key clinical outcomes including the extent of tumor destruction, potential complications, and prognosis.
4. Validation: validate the digital twin and outcome predictions using retrospective clinical data and prospective clinical trials.

Compétences

Technical skills and level required:

• Sound knowledge of numerical analysis for PDEs
• Sound knowledge of Machine Learning / Deep Learning with Artificial Neural Networks
• Basic knowledge of physiscs of electromagnetic wave propagation

Software development skills : Python programming, TensorFlow, Pytorch.

Relational skills : team worker (verbal communication, active listening, motivation and commitment).

Avantages

• 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 (after 6 months of employment) 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
• Social security coverage

Rémunération

2100 € gross/month