Post-doctoral Fellowship in Image Analysis

Країна: Бразилія;

Місто: São Paulo

Додана: 06.12.2018

Роботодавець: São Paulo Research Foundation (FAPESP)

Тип: PostDoc vacancy;

Дедлайн подачі: 20.03.2019


Medical imaging require the development of methods to improve accuracy in the image analysis results. Advances in medical image analysis provide such tools, but there is still an important gap regarding paediatric brain imaging, even though there is an increasing medical demand. This project aims at contributing to fill this gap, focusing on brain magnetic resonance imaging (MRI) of infants, newborns and premature babies, which raise specific issues due to the particular grey/white matter contrast related to the physiological myelination process, the very fast but not continuously observed evolution of the brain structures and possible pathologies, and the high intra-and intersubjects variability.

One of these issues is that the data is typically noisy, ambiguous, scarce in nature and sparse in time. In turn, expert medical knowledge is available, but is prone to change and evolution. From this point of view the project tackles one of the very cutting edge questions in data analysis, i.e. how to extract and understand meaningful patterns where the data is scarce but expert knowledge, continuously enriched, is available. We propose to develop structural representations of knowledge and image information in the form of graphs and hypergraphs, which will be exploited to guide spatio-temporal image understanding (segmentation, recognition, quantification, comparison over time, description of image content and evolution).

The aim is to develop computational methods to support diagnosis, pathology analysis and patients’ follow-up. Applications will include the analysis of hyperintensities on the white matter, the volumetry of corpus callosum and its evolution, and neuro-oncology with the study of the influence of tumors on surrounding structures over time.

About the institution

The University of São Paulo is the best ranked University in Latin America, being considered one of country's more prestigious educational institutions. The Data Science Group at IME-USP is a traditional machine learning research working on the field for more than 20 years with strong international collaboration.

About the project

The candidate will work on two connected projects co-funded by FAPESP (São Paulo Research Foundation) and ANR (Agence Nationale de Recherche - France). The FAPESP-ANR joint project is a collaboration that includes the following Institutions: USP (Institute of Mathematics and Statistics and School of Medicine), Albert Einstein Hospital - SP, ParisTech, Université Dauphine and Faculté de Médicine Paris-Sud. The project focuses in the development of computational tools for processing of MRI images and their integration with biological data. The project involves specialists in medical image analysis, structural knowledge representation and paediatric neuroimaging.

The selected candidate will be funded by a FAPESP fellowship with the one of the following conditions: Initial funding of 2 years (being possibly extended up to 4 years based on performance), fellowship BRL 90K per year (approx. USD 24K / year) plus overhead for travel expenses such as attending to conferences. More information is available at The grant may also cover expenses for moving to São Paulo/Brazil (including flight tickets).

The candidate may also apply for an extra period (4 months to 1 year) to do part of the research at the ParisTech, Université Dauphine or Faculté de Médicine Paris-Sud (FAPESP BEPE fellowship) as part of the grant (not included in the aforementioned 4 years).

Required Skills

PhD degree with strong background in mathematical modelling and programming (e.g. Computer Science, Engineering, Physics, Math). Research experience and publications in one or more of the following areas: image processing, computer vision, pattern recognition, machine learning, Oral and written communication skills (English).

Application details

Please send the following documents to

- CV;
- Summary of doctoral thesis and other relevant works;
- Two recommendation letters from former supervisors or professors of courses you took.

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