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EURAXESS

MSCA-COFUND-CLEAR-Doc - PhD Position #CD22-02: Spinal Cord Modeling: towards predicting the injury and the recovery

13/10/2022

Job Information

Organisation/Company
Université Gustave Eiffel
Department
TS2-LBA
Research Field
Biological sciences
Biological sciences » Biological engineering
Researcher Profile
First Stage Researcher (R1)
Country
France
Application Deadline
Type of Contract
Temporary
Job Status
Full-time
Hours Per Week
35
Is the job funded through the EU Research Framework Programme?
H2020 / Marie Skłodowska-Curie Actions COFUND
Marie Curie Grant Agreement Number
101034248
Is the Job related to staff position within a Research Infrastructure?
No

Offer Description

Spinal cord injury affects between 250,000 and 500,000 people each year. It is usually caused by a spinal cord compression due to medullar canal narrowing (eg: intervertebral disc swelling) or the penetration of vertebral bone fragment resulting from fractures (Burst). One of the main challenges following a trauma is to assess neurological damage in order to predict recovery and guide injury management.

Finite element (FE) models of the spinal cord are often used to investigate spinal cord injuries, and mechanical parameters such as stresses and strains of tissues are used to predict injury risk. The relevance of the model's prediction relies on the characterization of the tissue’s mechanical properties and especially on the material properties of gray and white matter (Fournely et al. 2020). In most spinal cord models, these materials are considered homogeneous, non-viscoelastic and their behavior is based only on tensile tests (Bailly et al. 2021, Ichihara et al. 2001). However, many studies have shown that in the brain these tissues presented a non-homogeneous, non-linear, viscoelastic behavior (Ramo et al. 2018), strongly dependent on loading conditions (compression, tension, shear), on drainage conditions (confined or not) and on strain rate (Budday et al. 2019).

Recent studies, carried out at several scales (axial compression, micro-indentation) were able to characterize and discriminate the behavior of white and gray matter and to corelate white mater rigidity variations with axonal microstructure (Weickenmeier et al. 2017). However, these improved knowledges have not yet translated into an improved spinal cord FE models. Moreover, the effect of local properties variations within the spinal cord on the risk of injury has not yet been investigated.

The objective of this project is to improve a numerical model of spinal cord in order to assess the effect of local mechanical properties variations and viscoelasticity on the prediction of the risk of neurological injuries. The project will be broken down into three sub-objectives:

Objective 1. Modeling the local behavior of the white and grey matter.

Indentation tests performed in the laboratory and confined compression (Yu et al. 2019) will be numerically reproduced using a poro-viscoelastic materials law initially developed for human brain tissue (Greiner et al. 2021). This model represents the network of cells embedded within the extracellular matrix and the free-flowing pore fluid Inverse method will be use to find the intrinsic parameter (permeability, shear moduli, nonlinearity, viscosity) to best fit indentation and confined compression results. First, sets of parameters will be developed for each areas of the spinal cord. Second, the parameters of the material law will be defined according to the structure of the indented tissues (myelin level, axonal density, etc.).

Objective 2. Modeling the overall behavior of the cord.

Published confined axial compression (Yu et al. 2019), transverse compression (Fradet et al. 2016) and relaxation tests on porcine spinal cord will be reproduced numerically using the material properties previously obtained. The effect of local refinement of material parameters (considering white and gray matter, microstructure, viscoelasticity, etc.) on the overall behavior of the cord will be evaluated.

Objective 3. Correlating the mechanical response of the spinal cord to neurological damage.

The stress and strain in the spinal cord model will be used to predict neurological lesions. For this, the injury threshold will be defined by reproducing porcine spinal cord impacts carried out at different speeds (Kim et al. 2018) and showing different injury severities. The effect of accounting for the microstructure in the modeling of the spinal cord on the prediction of the lesion will be evaluated by experimental design. Finally, the refined spinal cord model will be used to reproduce real cases of Burst-type fracture (Diotalevy et al. 2020) and predict the risk of injury. The prediction of the refined model will be evaluated in regard to the actual injury of patients and their recovery.

Requirements

Research Field
Biological sciences
Education Level
Bachelor Degree or equivalent
Skills/Qualifications
  • At the time of the deadline, applicants must be in possession or finalizing their Master’s degree or equivalent/postgraduate degree.
  • At the time of recruitment, applicants must be in possession of their Master’s degree or equivalent/postgraduate degree which would formally entitle to embark on a doctorate.
Languages
FRENCH
Level
Basic
Languages
ENGLISH
Level
Excellent

Additional Information

Benefits
  • High-quality doctoral training rewarded by a PhD degree, delivered by Université Gustave Eiffel
  • Access to cutting-edge infrastructures for research & innovation.
  • Appointment for a period of 36 months based on a salary of 2 700 € (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.
  • International mobility will be mandatory
  • An international environment supported by the adherence to the European Charter & Code.
  • Access to dedicated CLEAR-Doc trainings with a strong interdisciplinary focus, together with a Career development Plan.
Eligibility criteria

Applicants must fulfil the following eligibility criteria:

  • At the time of the deadline, applicants must be in possession or finalizing their Master’s degree or equivalent/postgraduate degree.
  • At the time of recruitment, applicants must be in possession of their Master’s degree or equivalent/postgraduate degree which would formally entitle to embark on a doctorate.
  • At the time of the deadline, applicants must be in the first four years (full-time equivalent research experience) of their research career (career breaks excluded) and not yet been awarded a doctoral degree. Career breaks refer to periods of time where the candidate was not active in research, regardless of his/her employment status (sick leave, maternity leave etc). Short stays such as holidays and/or compulsory national service are not taken into account.
  • At the time of the deadline, applicants must fulfil the transnational mobility rule: incoming applicants must not have resided or carried out their main activity (work, studies, etc.) in France for more than 12 months in the 3 previous years.

One application per call per year is allowed.

Applicants must be available full-time to start the programme on schedule (November 1st 2023).

Application rules are enforced by the French doctoral system which specifies a standard duration of 3 years for a full-time PhD together with the MSCA standards and the OTM-R European rules as follows.

Citizens of any nationality may apply to the programme.

There is no age limit.

Selection process

Please refer to the Guide for Applicants available on the CLEAR-Doc website

Additional comments
  • The First step before applying is contacting the PhD supervisor. You will not be able to apply without an acceptation letter from the PhD supervisor.
  • International mobility:Mobility is planned at École de Technologie Supérieure in Montreal (Canada) in the laboratory of Professor Yvan Petit.
  • Please contact the PhD supervisor for any additional detail on job offer.
  • There are no restrictions concerning the age, gender or nationality of the candidates. Applicants with career breaks or variations in the chronological sequence of their career, with mobility experience or with interdisciplinary background or private sector experience are welcome to apply.
  • Support service is available during every step of the application process by email: clear-doc@univ-eiffel.fr
Website for additional job details

Work Location(s)

Number of offers available
1
Company/Institute
Université Gustave Eiffel
Country
France
City
Salon de Provence
Postal Code
13300
Street
304 Chemin de la Croix Blanche
Geofield

Contact

City
Marne-La-Vallée
Website
Street
5, Boulevard Descartes
Postal Code
77454
E-Mail
nicolas.bailly@univ-eiffel.fr