RESEARCH FIELDPhysics › Biophysics
RESEARCHER PROFILEFirst Stage Researcher (R1)
APPLICATION DEADLINE26/02/2021 23:00 - Europe/Brussels
LOCATIONFrance › Evry
TYPE OF CONTRACTTemporary
HOURS PER WEEK35
OFFER STARTING DATE01/09/2021
EU RESEARCH FRAMEWORK PROGRAMMEH2020 / Marie Skłodowska-Curie Actions COFUND
MARIE CURIE GRANT AGREEMENT NUMBER945304
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: “Transfer learning in Biomechanics: High-dimensional transfer learning for personalized biomechanical modeling in surgery planning: application to anterior-cruciate ligament reconstruction”
Context – Motivation
Numerical modeling offers unprecedented possibilities for surgery planning by allowing quantitative patient-specific predictions, therefore reducing the risks of complications and improving the safety of the patients. While the first generation of models are solely based on bioimaging data, numerous surgical problems necessitate a more complex description that takes into account the mechanics and kinematics of the tissues and implants of interest, with patient-specific images as input data.
The reconstruction of the Anterior Cruciate Ligament (ACL), which is the most frequent ligament injury (incidence rate 1/3000), is an excellent illustration of this challenge. Recent works have shown that friction against the cartilage of the femoral notch and condyle is a major cause of failure for reconstructed ACL. The occurrence and intensity of friction is highly sensitive to the location of the femoral and tibial insertion points chosen by the surgeon and to the local bones shape. Such contact problem is extremely difficult to anticipate as it depends not only on the geometry but also on the kinematics of each knee. Today, the image-based biomechanical modeling procedure is far away too slow for any practical application to patient-specific modeling in view of surgery planning.
Scientific objectives, methodology & expected results
Our project proposes to tackle this issue by associating Artificial Intelligence approaches, model reduction and image-based biomechanical modeling to develop efficient numerical models for ACL reconstruction.
The objective is three-fold:
- to provide a clinically-relevant model applicable to fast surgical planning;
- to provide a deeper understanding of the damaging mechanisms of reconstructed ligaments;
- to develop deep transfer learning methods for image-based modeling in biomechanics. For that, input data coming from different sources will be used: radiographic data, magnetic resonance images, experimental data on animal models, data on the variability of surgeon accuracy.
The project will benefit from an established collaboration with the consortium of the LIGAGEL project, which aims at developing novel artificial ligaments for ACL reconstruction. This will provide access to complete data sets including 3D biomechanical simulations, 3D imaging and kinematics for both sheep and Human knee joints as well as post-operative observations of ligament and cartilage damage in the case of animal studies. This project will be a success if patient specific digital images and hyperelastic data, with different source domains, are merged in a fast image-based modeling chain, with a simulation speed up of 100.
In high-dimensional transfer learning, dimensionality reduction is one of the most important ways to preserve the discriminant information for subsequent classification or for model order reduction via a ROM-net. In reduced order models, both deep learning and physical equations are coupled in a single modeling procedure. It achieves transfer learning. The main advantage of transfer learning is its ability to reuse data related to various source domains, here biomechanics and image classification, when it is expensive or impossible to re-collect the needed training data for the target domain. Recent advances in U-nets and in multimodal autoencoders that extract a common latent space from various sources of data, will certainly foster fast image-based reduced predictions with transfer learning. A particular attention will be given to the description of the sensitivity to the femoral and tibial insertion points, to the bones geometry and to the mechanical behavior of theligament substitute.
The PhD student will join the “Artificial Intelligence for the Sciences” training program. All observational data and numerical being already available, we are confident that several papers will be published in 3 years. Moreover, we already have a good experience in U-nets and in transfer learning via ROM-nets for mechanical modeling.
The PhD student will have the opportunity to visit the Berkeley Institute for Data Science (USA): we plan to work with Stefan van der Walt on reproducible science in machine learning.
David Ryckelynck and Etienne Decencière
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).
- 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.
- 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.
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.
The PhD student will be hosted in both Centre des Matériaux and Centre de Morphologie Mathématique of Mines Paris - PSL.
The Centre des Matériaux, located at Evry, is a laboratory associated with the CNRS, employing around 200 people including 30 researchers, 50 ITA, 85 PhD students and 11 Post-Doctoral researchers. Research concerns biomaterials, engineering materials, microstructural characterization, numerical modeling at various continuous scales and related data science.
The Centre de Morphologie Mathématique, located at Fontainebleau, is a laboratory specialized in image pro-cessing. The research work conducted in this laboratory is based on mathematical morphology and deep learning. The fields of application are varied: multimedia, material sciences, electronics, bioimaging, medicine, industrial control.
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
YEARS OF RESEARCH EXPERIENCE1 - 4
REQUIRED EDUCATION LEVELComputer science: Master Degree or equivalent
REQUIRED LANGUAGESENGLISH: Excellent
- Master’s degree in biomechanics, or in machine learning, or in computer vision.
- Very good knowledge of python programming.
EURAXESS offer ID: 580741
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