2023-05849 - Internship - Numerical modelling for stenosis characterisation
Contract type : Internship
Level of qualifications required : Bachelor's degree or equivalent
Other valued qualifications : Master of engineering
Fonction : Internship ResearchContext
The project is an ongoing collaboration between Leiden University Medical Center (Leiden, Netherlands) and Inria team Simbiotx. It is part of the ERC European project MoDeLLiver (grant agreement id 864313). The intership will be co-supervised by Irene Vignon-Clémentel (Directrice de recherche) and Jérôme Kowalski (PhD student).Assignment
A crucial aspect in the surgical decision process is organ perfusion and functional assessment. In this context, a very active medical imaging field is dynamic functional imaging. Functional imaging often involves a tracer, which is transported in the blood circulation, with a certain time-dynamics as it goes through the different components of the circulatory system. Signals are often interpreted by ‘time-to-peak' or ‘area-under-the-curve' measurements that are non-specific markers of the underlying perfusion and function. This is inhibiting a wider clinical use of such modality. This internship project aims at better understanding these dynamical signals in the context of bad perfusion assessment in the case of a stenosis (abnormal narrowing a blood vessel) using mathematical modelling and numerical simulation of blood flow and transport.
For a better knowledge of the proposed research subject : 1 Vignon-Clementel et.al., A proof-of-concept pipeline to guide evaluation of tumor tissue perfusion by dynamic contrast-agent imaging: direct simulation and inverse tracer-kinetic procedures. Front. Bioinform. Sec. Computational BioImaging (soon printed)
2 Audebert et.al., Kinetic scheme for arterial and venous blood flow, and application to partial hepatectomy modeling. Computer Methods in Applied Mechanics and Engineering. 2017
3 Sourbron, S.P. and Buckley, D.L. (2013), Classic models for dynamic contrast-enhanced MRI. NMR Biomed., 26: 1004-1027. https: // doi.org/10.1002/nbm.2940
4 Project MoDeLLiver. NUMERICAL MODELLING OF HEMODYNAMICS AND PHARMACOKINETICS FOR CLINICAL TRANSLATION MoDeLLiver Project Fact Sheet H2020 CORDIS European Commission. url: https: // cordis.europa.eu/project/id/864313Main activities
Technical skills and level required : Python (advanced). Differential equations, numerical methods, C++. Git is a plus.
Languages : English (fluent)Benefits package
Theme/Domain : Computational Biology Biologie et santé, Sciences de la vie et de la terre (BAP A)
Town/city : Palaiseau
The ideal candidate will have a strong engineering background in applied mathematics/numerical methods/modelling (partial differential equations – Navier-Stokes/advection-diffusion equations; numerical methods to solve them), experience in coding (C++/Python) and a taste for multidisciplinary work. Experience in bio-engineering is a plus. Fluent English is a must.About Inria
Inria is the French national research institute dedicated to digital science and technology. It employs 2,600 people. Its 200 agile project teams, generally run jointly with academic partners, include more than 3,500 scientists and engineers working to meet the challenges of digital technology, often at the interface with other disciplines. The Institute also employs numerous talents in over forty different professions. 900 research support staff contribute to the preparation and development of scientific and entrepreneurial projects that have a worldwide impact.Instruction to apply
Defence Security : This position is likely to be situated in a restricted area (ZRR), as defined in Decree No. 2011-1425 relating to the protection of national scientific and technical potential (PPST).Authorisation to enter an area is granted by the director of the unit, following a favourable Ministerial decision, as defined in the decree of 3 July 2012 relating to the PPST. An unfavourable Ministerial decision in respect of a position situated in a ZRR would result in the cancellation of the appointment.
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