19/11/2020
The Human Resources Strategy for Researchers
Marie Skłodowska-Curie Actions

PhD position 08 – MSCA COFUND, AI4theSciences (PSL, France) - “Physically Informed Machine Leaning for controlling unruptured intracranial aneurysms”

This job offer has expired


  • ORGANISATION/COMPANY
    Université PSL
  • RESEARCH FIELD
    PhysicsComputational physics
  • RESEARCHER PROFILE
    First Stage Researcher (R1)
  • APPLICATION DEADLINE
    26/02/2021 23:00 - Europe/Brussels
  • LOCATION
    France › Sophia Antipolis
  • TYPE OF CONTRACT
    Temporary
  • JOB STATUS
    Full-time
  • HOURS PER WEEK
    35
  • OFFER STARTING DATE
    01/09/2021
  • EU RESEARCH FRAMEWORK PROGRAMME
    H2020 / Marie Skłodowska-Curie Actions COFUND
  • REFERENCE NUMBER
    AI4theSciences-PhD-08
  • MARIE CURIE GRANT AGREEMENT NUMBER
    945304

OFFER DESCRIPTION

“Artificial intelligence for the Sciences” (AI4theSciences) is an innovative, interdisciplinary and intersectoral PhD programme, led by Université Paris Sciences et Lettres (PSL) and co-funded by the European Commission. Supported by the European innovation and research programme Horizon 2020-Marie Sklodowska-Curie Actions, AI4theSciences is uniquely shaped to train a new generation of researchers at the highest academic level in their main discipline (Physics, Engineering, Biology, Human and Social Sciences) and master the latest technologies in Artificial Intelligence and Machine Learning which apply in their own field.

26 doctoral students will join the PSL university's doctoral schools in 2 academic cohorts to carry out work on subjects suggested and defined by PSL's scientific community. The 2020 call will offer up to 15 PhD positions on 24 PhD research projects. The candidates will be recruited through HR processes of high standard, based on transparency, equal opportunities and excellence.

 

Description of the PhD subject: “Physically Informed Machine Leaning for controlling unruptured intracranial aneurysms”

 

Context - Motivation

Cerebral aneurysms are balloonings of blood vessels in the brain and are thought to be present in 1 in 30 adults. Most of these aneurysms may never rupture, but when they do it is catastrophic: roughly half of patients die and of those that survive, roughly one-third will be permanently disabled.

Because brain imaging is being used widely as part of routine clinical workups, unruptured aneurysms are being detected more frequently. This poses a dilemma for the doctor and patient since the risk associated with treating the aneurysm can often be higher than the risk of rupture. Currently, doctors base their decisions on the size, shape, and location of the aneurysm, as well as family history and other risk factors such as smoking and hypertension. But these together are not strong predictors of rupture risk.

Research over the past decades has demonstrated that the hemodynamic forces exerted by the complex blood flow patterns within aneurysms may provide important clues as to the state of the aneurysm wall. Among several hemodynamic parameters that are discussed as key factors in the initiation, development, or rupture of intracranial aneurysms, one of the most studied parameters is the wall shear stress (WSS, the frictional forces exerted by flowing blood). High or low local values of the WSS and non-uniform distribution of instability are negative conditions for the development of an aneurysm.

Indeed low WSS may lead to spatial disorganization of endothelial cells and a disregulation of antioxidant and anti-inflammatory mediators result in arterial wall remodeling. Consensually, high WSS may lead to the initiation of aneurysm formation, but its influence on the growth and rupture is largely unknown.

Therefore, the availability of a simulation tool that can predict the aneurysm hemodynamics` parameters on an individual basis will be extremely useful, either to develop new devices, or to support treatment decisions.

Owing to the absence of precise in vivo measurement tools, computational techniques offer new capabilities in the healthcare provision for cerebral aneurysms[1]. Significant studies have been led by modeling the blood flow behavior using computational fluid dynamics (CFD) and Fluid-Structure interaction (FSI) simulations. CFD-FSI is a powerful tool not only used to design cars and airplanes but also in medical applications.

Nevertheless, these simulations require many assumptions, including the challenging patient-specific modeling of the vascular system, and require significant computational resources. Recent advances in the development of Deep Reinforcement Learning (DRL) algorithms have led to the rise of Deep Neural Networks (DNN), powerful tools capable of leveraging the ever-increasing volume of numerical and experimental data generated for research and engineering purposes into novel insight and actionable information.

State-of-the-art DRL techniques have proven fruitful for various applications, from solving computer vision problems to achieving super-human level in complex games, and have led breakthroughs in the optimal control of complex dynamic systems.

Given the ability of DNNs to handle large scale, non-linear systems, it is only natural to attempt to use them to tackle similarly the state-space models resulting from the high-dimensional discretization of partial differential equations (i.e. Navier-Stokes equations) [2]. Following these striking achievements, DRL has consistently spread to fluid mechanics, and has led to a handful of seminal, high-potential techniques addressing flow control and optimization problems.

 

Scientific Objectives, Methodology & Expected results

The proposed research aims then to bridge this gap between high fidelity CFD for the prediction of the aneurysm hemodynamics with the DRL for flow control and optimization which is importantly useful to reduce or to exclude the aneurysm from circulation [3] by employing the adequate stent for flow diversion. Therefore, the size, the location and the shape of such stent will be deduced by the proposed CFD-DRL framework. The deployment and the analysis of fifty consecutives patients with unruptured intracranial aneurysms, provided by the Neuroradilogy and Intervention Institute from the University Hospital-LMU Munich, will be included in this study and they will serve for our data sets, training, tests and validations.

These geometries have been reconstructed from MR angiography images acquired with 3D time-of-flight (TOF) technique. The resulting images will be subsequently segmented in order to extract the unruptured intracranial aneurysms. Different segmentation techniques will be explored in this purpose, based upon a novel approach recently developed for computing superpixel segmentations [4] and for merging superpixels together in order to get an actual segmentation of the aneurysms. Supervised approaches including convolutional neural networks will also be considered. This will definitely form the required data set for test and validations.

Finally, a qualitative comparison with the patient-specific digital subtraction angiography (DSA) will be performed. Predictions based on deep learning based CFD simulations can predict rupture risk of the aneurysm, anticipate an optimal hemodynamic pattern before the clinical procedure, consequently decreasing the complications, improving life quality and expectancy of patients, and ultimately helping saving lives.

The proposed work is highly multidisciplinary and the algorithms developed as a part of this research can be quickly adopted to a wide range of engineering and medical applications. The research will also greatly encourage candidates to pursue careers in interdisciplinary areas that bridge the biological, mathematical, and computational sciences.

 

Main references:

[1] Y. Qian, H. Takao, M. Umezu and Y. Murayama Risk Analysis of Unruptured Aneurysms Using Computational Fluid Dynamics Technology: Preliminary Results, American Journal of Neuroradiology November 2011, 32 (10) 1948-1955

[2] Optimization and passive flow control using single-step deep reinforcement learning, H. Ghraieb, J. Viquerat, A. Larcher, P. Meliga, E. Hachem, Submitted to Physical Review Fluids,https://arxiv.org/abs/2006.02979

[3] Kim, Heung Cheol et al. “Machine Learning Application for Rupture Risk Assessment in Small-Sized Intracranial Aneurysm.” Journal of clinical medicine vol. 8,5 683. 15 May. 2019, doi:10.3390/jcm8050683

[4] Chang, Kaiwen, and Bruno Figliuzzi. "Fast Marching Based Superpixels Generation." International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing. Springer, Cham, 2019.

 

International mobility

Several short and long term visits of the PhD candidate to the Institute for Diagnostic and Interventional Neuroradiology at University Hospital-LMU Munich (Germany) are envisaged.

 

Thesis supervision

Elie Hachem and Bruno Figliuzzi

 

PSL

Created in 2012, Université PSL is aiming at developing interdisciplinary training programmes and science projects of excellence within its members. Its 140 laboratories and 2,900 researchers carry out high-level disciplinary research, both fundamental and applied, fostering a strong interdisciplinary approach. The scope of Université PSL covers all areas of knowledge and creation (Sciences, Humanities and Social Science, Engineering, the Arts). Its eleven component schools gather 17,000 students and have won more than 200 ERC. PSL has been ranked 36th in the 2020 Shanghai ranking (ARWU).

More Information

Benefits

  • Opportunity to conduct academic research in a top 100 university in the world.
  • High-quality doctoral training rewarded by a PhD degree, prepared within Mines Paris - PSL and delivered by PSL.
  • Access to cutting-edge infrastructures for research & innovation.
  • Appointment for a period of 36 months (job contract delivered by the involved component school of PSL) based on a salary of 3100 € gross employer (including employer tax) per month or approximately a 2228 € gross salary per month.
  • Job contract under the French labour legislation in force, respecting health and safety, and social security: 35 hours per week contract, 25 days of annual leave per year (“congés annuels”). Eventual complementary activities may be accepted or proposed by the co-supervisors (maximum of 64h/year for teaching, 32 day/year for specific missions).
  • Short stay(s) or secondment in France or abroad are expected.
  • An international environment supported by the adherence to the European Charter & Code.
  • Access to AI training package, with a strong interdisciplinary focus, together with a Career development Plan.

Eligibility criteria

  • Applicants must have a Master’s degree (or be in the process of obtaining one) or have a University degree equivalent to a European Master’s (5-year duration) to be eligible at the time of the deadline of the relative call;
  • There is no nationality or age criteria, but applicants must not have resided or carried out their main activity (work, studies, etc.) in France for more than 12 months in the 3 years immediately before the deadline of the call (MSCA Mobility rule);
  • Applicants must declare to be available to start the programme on schedule.

For submitting your online application, go to: https://www.psl.eu/recherche/grands-projets-de-recherche/projets-europee...

 

The online application should contain the following documents:

  • English translated transcripts from the Master’s degree (or equivalent 5-year degree). A copy of the Master’s degree or a certificate of achievement will be required later on for the final registration.
  • International curriculum vitae and a cover letter explaining the reasons that lead him/her to prepare a PhD, why he/she applies to this offer and his/her professional project (guidelines will be given to the applicants in order to help him/her in the writing of his/her letter).
  • Two academic reference letters.
  • A statement duly signed on the mobility rules, availability, and conflicts of interest.

 

The applicants can only apply to one PhD project among the available ones. Multiple applications of one candidate will automatically make all his/her applications ineligible.

Selection process

The applications will be analysed by the Management Team for eligibility and completeness. Afterwards, the applications will be reviewed by the Selection Committee. In the pre-selection round (March-April 2021), applicants will be rated using a scoring system based on 3 criteria (academic excellence, experience, motivation, and qualities). A shortlist of qualified applicants will be interviewed during the selection round (June 2021) to further assess their qualifications and skills according to the predefined selection criteria.

All information regarding the applications (criteria, composition of the Selection Committee, requirements) can be found on the website of the programme, in greater detail.

 

The selection and recruitment processes of the PhD student will be in accordance with the European Charter for Researchers and Code of Conduct of the Recruitment of Researchers. The recruitment process will be open, transparent, impartial, equitable, and merit based. There will be no discrimination based on race, gender, sexual orientation, religion of belief, disability, or age.

Additional comments

CEMEF (Centre for Materials Processing) a joint unit with CNRS (UMR 7635) is a research center of Mines Paris - PSL. It is devoted to the physics, chemical physics, mechanics and numerical modelling of materials processing. The project will be conducted within the CFL research groupat CEMEF in interaction with the CMM research center as well as the Neuroradilogy and Intervention Institute at Munich.

 

Mines Paris - PSL is a graduate school (Grande Ecole) in Science, Engineering and Economics under the tutelage of the Ministry of Industry. It provides multidisciplinary education (relying upon around 230 academics) to train high level engineers (150/year in “Master’s degree in science and executive education” track and 200 Advanced Master’s degrees/year) expected to fulfil high responsibilities in government bodies and in the industry in France and abroad. This mission is connected to a very strong research activity closely linked to the industry, that has proved its ability to innovate, anticipate and contribute to the evolutions of the socio-economic world in the fields of risk management, public policies, economics et sustainable development among others.

This long-standing link with the industry has lead Mines Paris - PSL to set up several major collaborations (Research and Education Chairs, Framework agreements…) with leading industrial partners such as SAFRAN, EDF, ArcelorMittal or Total to name a few.

 

On the academic side, it is a full member of Université PSL and as such participates to a wide number of structuring education and research (both applied and more fundamental) projects, especially with two other Grandes Ecoles (Chimie Paris - PSL and ESPCI - PSL). Mines Paris - PSL is also leader or acting member of several major international academic research programs and projects on its own.

Web site for additional job details

Required Research Experiences

  • RESEARCH FIELD
    Physics
  • YEARS OF RESEARCH EXPERIENCE
    1 - 4

Offer Requirements

  • REQUIRED EDUCATION LEVEL
    Physics: Master Degree or equivalent
  • REQUIRED LANGUAGES
    ENGLISH: Excellent

Skills/Qualifications

  • Master of Science or equivalent in Applied Mathematics, Physics, or Mechanical Engineering.
  • Competences in fluid dynamics, statistics, or scientific computing.
  • Good experience in programming (C, C++) and in data post-processing and analysis.
  • Excellent writing skills, fluent in English.
  • Rigorous, autonomous, creative and motivated by working at the edge between basic research and industrial applications.

Work location(s)
1 position(s) available at
CEMEF, Mines Paris - PSL
France
Sophia Antipolis
06904
1, rue Claude Daunesse

EURAXESS offer ID: 578994

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