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Nanoparticle tracking across biological membranes using nuclear imaging (PET) for medical physics applications

Centre de Recherche du CHU de Québec

Saturday, August 15, 2020 Funded PhD Project (Students Worldwide)

About the Project

The Laboratory for Biomaterials in Imaging at the CR-CHU de Québec - Université Laval is seeking highly qualified MSc graduates (or the equivalent) for a PhD project aiming at measuring the kinetics of gold nanoparticle permeation across biological membranes, for medical physics applications. PhD candidates with demonstrated experience in nanoparticle synthesis, nanomaterials analysis, biomedical imaging, cell culture experiments and other related fields, are encouraged to apply. In this project, the candidate will be involved in a translational research project aiming at measuring the optimal nanoparticle elution time maximizing the diffusion of therapeutic particles in surface tissues, prior to radiation therapy. The PhD student will have access to state-of-the-art small-animal imaging facility (PET, MRI, CT), as well as to nanomaterials analysis equipment (TEM, SEM, XPS, FTIR, DLS, etc) and cell culture infrastructure for oncology and regenerative medicine. The biomedical imaging analyses will be coupled to advanced electron microscopy results in order to develop a comprehensive dosimetry approach integrating the impact of gold nanoparticles as radio sensitizers in oncology.
*** In the current Covid-19 pandemic situation, Canadian citizens, residents of Canada, and of countries eligible to travel to Canada before Octo 1st, are encouraged to apply.

The applicants should sent their CV, academic grades, and motivation letters and the names + addresses of two references.

Funding Notes

This project is funded by a NSERC grant (MAFortin). The stipend is fixed according to the general guidelines of the Fonds de la Recherche en Santé du Québec (FRQS).


Relevant reference from the research group: Intratumoral Injection of Low-Energy Photon-Emitting Gold Nanoparticles: A Microdosimetric Monte Carlo-Based Model; Fortin et al: ACS Nano 2018, 12, 3, 2482–2497

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