Jobs

You find here internship opportunities, theses, post doctorates, temporary and permanent vacancies in partner labs network.

Liste des annonces

2020/09/15 – Post doctorant.e UHF MRI

Motion correction for brain Magnetic Resonance Imaging at high and ultra-high field.

We are looking for a curious, motivated, team-oriented candidate with a PhD degree in signal processing, physics, computer science, applied mathematics, biomedical engineering or related topics. Prior knowledge in magnetic resonance parallel imaging acquisition and reconstruction techniques would be advantageous.
The successful candidate will work in the central nervous system team composed of physicists, neuroscientists, clinicians and computational scientists.
Salary is based on previous experience (2.7-3.2 k€ monthly gross salary).
The position is granted by Aix Marseille University (www.univ-amu.fr) in a partnership project with
Siemens Healthineers. The work will be done at the CRMBM laboratory (www.crmbm.univ-amu.fr) which is located in the center of the lively Marseille city, within La Timone university hospital.

2020/06/17 – Thesis in Positron Emission Tomography Image Reconstruction

Context and objectives.

Nuclear imaging, especially positron emission tomography (PET), is a powerful tool of nuclear medicine in oncology. The development of multimodality (PET-CT, PET-MR) along with advances in PET technology enable ever-increasing precision and accuracy in the quantification of molecular processes in vivo. However, PET measurements do not directly lead to images and a complex inverse tomographic problem has _first to be solved. This task of tomographic reconstruction is essential in PET as it may have a significant impact on image quantification, and thus the outcome. In this context, this PhD project focus on the development of innovative tomographic image reconstruction methods in order to tackle current and future challenges raised by applications where the recorded signal is weak.
Our team has a recognized expertise in 90Y PET imaging for postinfusion quantitative assessment following radioembolization therapy in liver cancer [1]. The challenge of this application is to be able to evaluate the delivered dose inside and outside the targeted lesions, from the reconstruction of the
90Y PET signal [2]. This signal is characterized by very few interesting events hidden in a high level of background events [3, 4], making the tomographic reconstruction problem very ill-posed. From current state-of-the-art methods, reconstructed images su_er from high levels of noise and local bias, which do not allow for precise dosimetry.
In the continuation of the efforts lead by the team, the candidate will work on the development and evaluation of methods adapted to this challenging application. The PhD project has two main objectives.
The first objective is to build innovative PET reconstruction algorithms allowing to further optimize the compromise between bias and noise, as compared to currently investigated algorithms. These algorithms could be driven by the flexible -divergence [5] and include a penalty term based on the recently proposed deep image prior [6]. The second objective is to develop a method able to associate confidence values on dosimetry measurements extracted from the reconstructed PET images. As PET images intrinsically suffer from inaccuracy and/or imprecision, being able to deliver confidence values or intervals associated to them seems natural but is a challenge. The developments would be based on recently proposed bayesian posterior bootstrap methods [7] that provided promising preliminary results when applied to PET reconstruction.
Current protocols are running on PET/CT scanners. A hybrid PET/MR scanner will be installed in 2021 at the University Hospital. Future patients treated with 90Y will benefit from the use of this scanner for improved liver imaging thanks to MR. The use of MR information in the PET reconstruction process and the estimation of confidence values is of particular interest. Taking advantage of multimodality will be an integrated part of the PhD project.

The position is funded by iemens Healthineers (these Cifre). The collaboration between Siemens Healthineers and the University Hospital is of long duration. The project will also be conducted in partnership with the Numerical Science Laboratory in Ecole Centrale Nantes and will be associated with the work done by a current PhD student.

2020/05/05 – Doctorat “Dosimetric improvements for selective internal radiation therapy of hepatic tumours and impact on patient response”

Liver cancer is the sixth most common cancer in the world but the second leading cause of cancer mortality in men. Among the different types of liver cancer, some can be treated by selective internal radiation therapy (SIRT), which consists in injecting Yttrium-90 (90Y) β-emitter microspheres into the liver. This project aims at improving SIRT treatments by bringing state-of-the-art dosimetric techniques to SIRT that will be validated through Monte-Carlo simulations, and developing deep learning methods to predict treatment response from previous dosimetric models.
The 90Y microspheres injection treatment has multiple steps. An acquisition of magnetic resonance imaging (MRI) is performed, followed by a simulation of the treatment via a technetium-99m macroaggregated albumin (Tc99m-MAA) single-photon emission computed tomography (SPECT)/CT scan. This SPECT/CT scan leads to compute a pre-treatment dosimetry and allows to determine the amount of 90Y microspheres needed for the treatment. Right after the injection of 90Y microspheres, a positron emission tomography (PET)/CT scan is acquired to compute a 90Y-treatment dosimetry. This dosimetry should be as accurate as possible to document the actual dose delivered to the targets in SIRT. Besides, there is increasing evidence of a dose-effect relationship in the case of SIRT. However, dosimetric results are known to be very sensitive to technical factors (acquisition, reconstruction, segmentation, dosimetric models) and, in the absence of standardised techniques, a bunch of dosimetric thresholds has been reported in the literature. Until recently, most of the dosimetry models have been carried out using empiric formulae or simplified dosimetric models.
OBJECTIVES
The first aim of this work is to validate a more accurate dosimetric model for both the Tc99m-MAA-pre-treatment and the 90Y-post-treatment by using a Monte-Carlo approach with the GATE toolkit. This validation implies simulations of physical phantoms representative of the liver uptake in SIRT and comparisons with state-of-the-art techniques such as the voxelized dosimetry used in clinical routine.
Based on the data collected since 2012, the second aim of this work is to develop a supervised deep learning classification approach to predict the treatment response using either the Tc99-MAA-pretreatment dosimetry or the 90Y-treatment dosimetry or both and assess correlations from our validated dosimetric calculations.

2019/12/20 – Stage “Classification d’images rétiniennes par apprentissage profond pour l’aide au diagnostic en ophtalmologie”

Contexte
Dans le domaine médical, l’analyse d’images fondée sur l’apprentissage profond (Deep Learning) a conduit récemment à des avancées significatives dans le dépistage précoce de pathologies, dans l’aide au diagnostic, ou dans la segmentation de structures anatomiques. Ce domaine de recherche très actif devrait conduire dans les prochaines années à une forte modification de l’exploitation et de l’interprétation des images médicales par les médecins spécialistes en imagerie médicale.
En ophtalmologie, des travaux innovants en classification par « Deep Learning » ont récemment été menés [1-3] pour le dépistage et le diagnostic de deux pathologies chroniques : la Rétinopathie Diabétique (RD) et la Dégénérescence Maculaire Liée à l’Âge (DMLA), qui sont à l’origine d’un déclin irréversible de l’acuité visuelle. Nos travaux [4] s’inscrivent dans ce contexte et exploitent une importante base de données constituée d’images de fond d’œil et d’images acquises par une nouvelle
technique d’imagerie rétinienne : l’OCTA (Angiographie par Tomographie de Cohérence Optique).

Objectifs du stage
Nos travaux en Deep Learning se fondent sur une importante base de données d’images OCTA récemment constituée dans le cadre d’un partenariat avec le service d’Ophtalmologie de l’Hôpital Intercommunal de Créteil (CHIC) qui est une référence dans le domaine des pathologies rétiniennes.
Sur cette base de données, nous avons mis en œuvre plusieurs architectures de réseaux de neurones pour classifier les images OCTA et obtenu d’excellents résultats dans la différenciation des cas sains et pathologiques.
L’objectif du stage est d’exploiter cette base de données et les architectures de réseaux de neurones développées afin d’identifier des cartes de caractéristiques (« feature maps ») permettant de discriminer différents types de pathologies rétiniennes.

Durée du stage : 5 mois, à partir de Février ou début Mars 2020.

Références bibliographiques
[1] S. Saha, M. Nassisi, M. Wang, S. Lindenberg, S. Sadda, and Z. J. Hu, ‘Automated detection and classification of early AMD biomarkers using deep learning’, Scientific reports, vol. 9, no. 1, pp. 1–9, 2019.
[2] F. Li et al., ‘Deep learning-based automated detection of retinal diseases using optical coherence tomography images’, Biomedical Optics Express, vol. 10, no. 12, pp. 6204–6226, 2019.
[3] A. Kushwaha and P. Balamurugan, ‘Classifying Diabetic Retinopathy Images using Induced Deep Region of Interest Extraction’.
[4] K. Taibouni, Y. Chenoune, A. Miere, D. Colantuono, E. Souied, and E. Petit, ‘Automated quantification of choroidal neovascularization on Optical Coherence Tomography Angiography images’, Computers in biology and medicine, vol. 114, p. 103450, 2019.

2019/12/20 – MRI Application Scientist at Bruker

You will be integrated into our international application team, which will bring you in contact with the leading experts in the field and will require close co-operation with colleagues in the integration, soft- and hardware departments.

The tasks include:
Demonstration of our preclinical imaging systems, in particular MRI
Creating protocols, workflows and optimizing application sequences
Customer training worldwide (with a focus on Europe)
Customer teaching (in-house, webinars)
Sales and after sales support (e.g. customer hotline, customer visits)
Support of system integration and component specification
Creation of user documentation and training material
Marketing support (marketing materials, lectures, and data preparation)
Assist in trade shows and conferences worldwide
Working with small rodents

Position opened since 12/18/2019
Type of position - Regular Full-Time

2019/12/11- Ingénieur-e biologiste en plateforme scientifique au CRMBM (mobilité interne au CNRS)

Un poste d'ingénieur(e) biologiste en plateforme scientifique est à pourvoir au CRMBM.
L'activité sera effectuée sur la plateforme d'imagerie du petit animal du CRMBM, localisée à la Faculté de Médecine Timone, en interface avec les trois équipes cerveau, coeur et muscle et les fonctions supports ainsi qu'en interaction avec les collaborateurs de ces équipes et utilisateurs extérieurs (académiques, industriels). La plateforme d'imagerie du petit animal dispose de trois spectromètres imageurs IRM multinoyaux à 4,7T, 7T et 11,75T.

L'ingénieur(e) sera sous la responsabilité de Mme Angèle Viola, directrice de recherche au CNRS et responsable de l'activité cerveau petit animal, en interaction avec les responsables des équipes cœur et muscle du laboratoire ainsi qu'avec l'équipe support. Une contribution aux actions collectives du laboratoire (démarche qualité, tâches associées à l'expérimentation animale, maintenance des aimants est attendue).

Missions :
L'ingénieur-e devra concevoir, développer et conduire de protocoles d'imagerie et de spectroscopie par résonance magnétique (IRM/SRM) pour l'exploration de modèles animaux (rats et souris) de pathologies humaines affectant le système nerveux central, le muscle et le cœur.
Activités :
Concevoir des protocoles d'acquisitions IRM/SRM multiparamétriques sur rongeurs pour répondre aux objectifs des projets de recherche des chercheurs du laboratoire ou des équipes partenaires Conduire ces acquisitions IRM/SRM in vivo de modèles animaux (rongeurs) de pathologies humaines (systèmes nerveux central, cardiovasculaire et musculaire) en relation avec les projets des utilisateurs ou partenaires Evaluer les paramètres physiologiques et cliniques des animaux Mettre en place les outils de traitement des données d'imagerie et spectroscopie et prendre en charge ce traitement Participer à l'analyse des données, au choix des méthodes et à leur mise en oeuvre Conseiller les utilisateurs et les partenaires sur les techniques disponibles et l'interprétation des données Assurer la veille scientifique et technologique en imagerie et spectroscopie du petit animal Mettre en œuvre les principes de la démarche qualité mise en place au laboratoire Animer des actions de formation Appliquer et faire appliquer la règlementation en matière d'éthique animale Participer aux tâches communes liées à l'infrastructure animalerie et à la maintenance des aimants

https://mobiliteinterne.cnrs.fr/afip/owa/consult.affiche_fonc?code_fonc=E56009&type_fonction=FS EP&code_dr=12&code_bap=&code_corps=IR&nbjours=&page=1&colonne_triee=1&type_tri=ASC

2019/10/18 – Research Engineer Position (2 years)

In the frame of the MULTIMOD program supported by the Regional Council and the European Union, ICMUB is seeking a highly motivated candidate with strong chemistry and bioconjugation background to design, prepare and characterize protein or peptide-based conjugates for imaging/therapy purposes. The selected candidate will have the opportunity to closely interact with the local imaging cluster and the preclinical imaging platform members. He/she will contribute to the team’s mission of delivering in time, fully defined conjugates to our academic/industrial partners.

The successful candidate will have the following responsibilities:
- Involvement in various R&D and public/private programs
- Synthesis of bioconjugates, by modification of home-made proteins/peptides or coming from our partners
- Development and implementation of robust analytical methods to fully characterize antibody-based conjugates
- Synthesis of specific molecular probes, in collaboration with researchers involved in technology transfer programs
- Preparation of technical documents, summary reports and regular updates on project status
- Participation to valorization actions.

Starting date: Asap

2019/10/09 – Thèse en développement de séquences IRM paramétriques à 3T

Au sein du laboratoire, l’équipe IRM 4D a l’expertise du développement de séquences IRM haute-résolution, à encodage non-cartésien. De plus, notre singularité provient du développement de séquences paramétriques, permettant ainsi la mesure de temps de relaxation.
L’un des objectifs de l’équipe IRM4D est de développer des séquences IRM chez l’homme pour aider au diagnostic, pronostic et suivi des patients. La quantification de paramètres physiques est particulièrement utile pour comparer des patients ou pour évaluer objectivement l’évolution d’une maladie.
Cependant, de nombreux verrous sont à prévoir comme les mouvements respiratoires et/ou la durée des acquisitions, qui doit être drastiquement réduite.
Par exemple, notre équipe a récemment implanté une séquence paramétrique à 3T, combinée à une accélération par Compressed Sensing pour la Neuro-imagerie.
Le doctorant aura pour objectif de poursuivre le développement de cette séquence et de la combiner avec différents modules ou acquisitions afin de surmonter les verrous rencontrés. Il sera en charge du développement des séquences, jusqu’à l’imagerie de volontaires sains.

2019/10/01 – Clinical Data Science Engineer in Neurospin (CEA-Saclay)

NeuroSpin is seeking to strengthen its commitment to clinical research by recruiting a Data Science Engineer to act as an interface between methodological teams and clinician-researchers to develop analyses dedicated to projects in adult neurology, psychiatry and brain development pathologies.
NeuroSpin's methodological teams are at the origin of the development of very high level platforms for the analysis of anatomical, micro-structural, functional and connectivity brain images, for methodological and cognitive neuroscience research in adult populations with "normal" brains (healthy subjects, psychiatry, etc.). However, clinical research questions, in adults and children alike, require more or less complex methodological adaptations and the use of multiple tools to account for the atypical or abnormal shapes and/or signals of the brains studied (brain lesions - atrophies, or tumors, signal anomalies, and modifications related to brain immaturity in children), small samples, suboptimal quality data (movements), missing data (e.g. non-cooperation of subjects).
This clinical interface engineer position, on permanent contract, aims to contribute to the transfer to clinical research in adults and children of state-of-the-art methods for the analysis of brain images, whether developed at NeuroSpin or in other centers: morphometry, structural connectivity and cyto-architectony (diffusion), functional MRI, functional connectivity, adapted statistics, etc.
The engineer, in direct contact with clinical researchers within the Clinical research (UNIACT) lab, will act in close interaction with the methodological teams within the framework of a transversal methodological support group in order to facilitate interactions between teams and to enrich the support for image analysis (availability, documentation, updating, adaptations, assistance in handling... selected methodological tools).

2019/10/03 – PhD position in preclinical nuclear imaging :”Development of theranostic agents for nuclear medicine”.

One of the aims of the laboratory is to develop radiopharmaceuticals suitable both for the diagnosis and therapy of tumor, or so called theranostic agents. The diagnosis is performed using the radiopharmaceutical coupled with a radioisotope for SPECT or PET nuclear imaging, while the therapy is performed using the same radiopharmaceutical coupled with a beta- emitter. The laboratory has recently developed a radiotracer directed against the mesothelin, a protein that is overexpressed in several cancer types while being only minimally expressed in healthy tissues. A first radiotracer dedicated to the diagnostic imaging of mesothelin has been previously validated in the laboratory. The objective of the candidate will be to further develop this radiotracer and to develop a theranostic agent. The candidate will be in charge of the biological evaluation of the radiotracers.

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