Marie Skłodowska-Curie Actions

MSCA-COFUND-CLEAR-Doc - PhD Position #CD21-58: External and internal statistical shape modelling for applications in biomechanics

This job offer has expired

    Université Gustave Eiffel
    EngineeringMechanical engineering
    First Stage Researcher (R1)
    21/03/2022 17:00 - Europe/Brussels
    France › Lyon
    H2020 / Marie Skłodowska-Curie Actions COFUND


Highly automated vehicles (HAVs) will be part of a future smart city. They should be not only comfortable for passengers/occupants, but also safe for both in-vehicle passengers and out-vehicle road users (e.g. pedestrians, cyclists). Although autonomous cars should reduce the amount of accidents by strictly following the traffic rules, road accidents will not disappear due to the coexistence of human drivers and automated systems, the malfunction of automatic driving technology, as well as the cases that cannot be addressed by current technologies. New technologies will also offer new opportunities to improve and to individualize the protection systems, and help with the vehicle acceptance. Thanks to onboard sensors and advanced IoT (Internet of Things) technologies, real-time monitoring of in-vehicle passengers and out-vehicle road users will be possible (Zhao et al, 2018). Combined with real time assessment of injury risks with help of computational human body models (HBM), this could allow adapting in real time the protection systems to the situation and to the specificities of the road user. This could bring benefits especially to road users whose characteristics differ from the average male currently used as the reference when designing safety systems (Beillas et al., 2009). For this purpose, we need information about the person (e.g. body size, and posture) to be protected (Grébonval et al., 2021). However, only external body shape can be monitored with computer vision approaches. The resulting skeleton model is extremely simplified and far from the real anatomical structures, while a reliable injury assessment using HBMs requires a better knowledge of the skeleton. One of challenging issues is to estimate internal skeleton position and characteristics from external body shape related information.

Since the first large scale anthropometric survey using a 3D body scanner in late 1990s, many similar anthropometric data were collected in different regions of the world. Statistical human body shape models are now available mostly in standing posture (Shu et al, 2012) but rarely in seated posture (http://humanshape.org/ by UMTRI). In health, a large amount of imaging data is available today and has enabled the development of segmentation algorithms based on learning linked with statistical shape models, mainly for bones. However, these data are typically obtained in the supine position and are focused on subparts of the skeleton, never the whole body.

In any case, if external and internal models exist, there is no statistical shape model of the whole body that includes external and internal structures and that can be realistically positioned for biomechanical simulations.

However, the development of such an internal & external model presents a number of challenges and gaps. For the skeleton, existing results are partial. Research efforts are presently focused on the creation of local statistical shape models (femur, knee joint, etc.). The LBMC contributes to this effort with the creation of statistical shape models (pelvis and femur, Savonnet et al., 2019, but also thorax, lower limb, skull). The assembly of these parts from different populations/data sources remains a scientific challenge to ensure consistency and relative positioning of anatomical structures. A strong potential exists with open databases such as the NMDID (https://nmdid.unm.edu/), including indicators of bone quality or cortical bone thickness.

Concerning the modeling of the whole body external shape, several statistical models exist, such as the one developed at LBMC recently in partnership with an industrial company. This type of model is typically based on a limited number of postures. While these models can be articulated using numerical methods from the computer graphics industry (i.e. skinning), the result is not necessarily anatomically relevant.

The assembly of internal and external shape models is also a complex task (besides the supine position) due to the lack of the relationships between external and internal structures. Such relationships exist for some restricted regions or joints (Peng et al, 2015; Nérot et al., 2016). However, linking these statistical models into a full body one would require an extended validation work to ensure the relevance of such a combination. The change from supine to a given position (e.g. seated) is also of particular interest as imaging data are mainly available only for the supine position.


The main objective of the thesis will be to develop a first whole body parametric model including both internal and external structures, fully articulated with statistical shape models of the components. The model will be published under an open license to promote its improvement and its applications in many fields related to biomechanics. It will thus be able to serve as a structuring research platform for the HBM research community.

The scientific issues that will be addressed during the thesis will include the preservation of coherence between internal and external shapes during repositioning, the assembly of data from different sources or postures, and the validation needs of such models.

During the thesis, the whole body parametric model will be connected to the PIPER open source framework and models (http://www.piper-project.eu/) in order to verify its performance and demonstrate its use in seating comfort (Wang et al., 2019) and safety applications.


  • After a review of existing models, data and methods, the following phases are planned:
  • Complete and assemble statistical shape models of the skeleton and outer skin
  • Articulate the skeleton models and develop anatomically correct skin deformation methods when changing posture
  • Assembly internal and external models by considering constraints and external/internal relationships under an optimization framework. These internal & external constraints can be enriched by collecting new data.
  • Refinement and validation from experimental data.
  • Safety and comfort applications with existing HBMs.


Beillas et al. (2009). Stapp Car Crash J. Nov;53:127-54.

Grébonval et al. (2021). PLoS One 16(9): e0257292. doi: 10.1371/journal.pone.0257292

Nerot et al. (2016) J. Biomechanics 49, 3415–22. .

Peng et al. (2015) J. Biomechanics 48 , 396-400

Savonnet t al. (2019) PLoS ONE 14(8):e0221201.

Shu et al. (2012) Int. J. of Human Factors Modeling and Simulation, vol. 3, no2, p. 133-146.

Wang et al. (2019) DHM and posturography, Academic Press, 643-659.

Zhao et al. (2018) SAE Technical Paper 2018-01-0505. SAE Technical Paper 2018-01-0505"

International Mobility

A 3-month secondment at Tongji University (China).

For more information, contact the PhD thesis supervisor.


More Information


  • 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

  • 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 not have resided or carried out their main activity (work, studies, etc.) in France for more than 12 months in the 3 years immediately prior to the call deadline.
  • Applicants must be available to start the programme on schedule (around 1st October 2022).

Selection process

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.
  • 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


Web site for additional job details

Offer Requirements

    ENGLISH: Excellent


  • 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.


Work location(s)
1 position(s) available at
Université Gustave Eiffel
25 Avenue François Mitterrand

EURAXESS offer ID: 717586


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