Position: A full-time post-doctoral research position is open at IMT Mines Alès – Alès, KID team (Knowledge representation and Image analysis for Decision).
Date: The position will start in fall 2019. Funding is for 6 months.
Consortium: The position is at IMT Mines Alès
Advisors: The Post Doc will be advised by:
-Gérard Dray (HDR)
Hepatic transplantation provides an indisputable benefit in terms of survival and quality of life for patients with significant hepatic impairment. The improvement in transplantation results over the last decade has led to its expansion. At the same time, it quickly became apparent that the number of patients who could benefit from transplantation consistently exceeds the number of organ donors.
The lack of donors has led to the widening of the selection criteria for hepatic grafts. This extension includes living and cardiac death donors. In order to avoid a transplant rejection, the transplanted liver must be contaminated by the steatosis as little as possible. Steatosis is the accumulation of lipid droplets within the cytoplasm of hepatocytes. The risk of primary non-functioning of the graft depends on the extension of steatosis. Steatosis is probably the most common cause of early dysfunction or primary non-function of the graft. It is also the most common cause of rejection of the organ during harvesting and therefore a subsequent reduction in the number of transplantable grafts.
The objectives of the PostDoc are to investigate and create novel computational models.
-A rapid state-of-art of computational models in medical diagnosis;
-Detect the liver in images;
-Investigate calculate the steatosis rate through image analysis, feature extraction, texture analysis and machine learning;
-Investigate the classification model on other organs;
-Define the most promising approaches/methods for implementing the topics above.
First, the objective is to extract desired organs images (liver for example) using image segmentation techniques. Therefore, texture analysis of the segmented regions will be useful for machine learning process and qualify the organ quality in order to avoid a transplant rejection. Finding the pertinent features that permit to predict the liver’s quality will be the key activity of this PostDoc. Existing Matlab functions will be used and other will be rigorously implemented. Indeed, as people’s lives will depend of the proposed technique, quantitative and qualitative results are essential to validate the proposed model.
– A PhD in Machine Learning with skills in Image and Signal Processing and Applied Mathematics – Strong mathematical background – geometry – linear algebra – optimization techniques
– Strong coding skills (Matlab/C++ )
– Language requirements: fluent spoken English or French, and fluent written English
– The candidate should have preliminary experience in at least two of the following areas: image processing –machine learning.
How to apply:
Please send your application including
to the 3 following e-mail addresses:
(c) GdR 720 ISIS - CNRS - 2011-2019.