---------------------------------------------------------------------------------------------------------------------------------- Open post-doc position at Géoazur in collaboration with Inria, at Sophia Antipolis, France, in the research area: Curvilinear network detection on satellite images using AI, stochastic models and deep learning.

EXTENDED Submission deadline July 31, 2019 ----------------------------------------------------------------------------------------------------------------------------------

Open Position for a post-doc scientist at Géoazur (https://geoazur.oca.eu/fr/acc-geoazur) in collaboration with Inria (https://www.inria.fr/en/centre/sophia), at Sophia Antipolis (Nice region), France, in the area of Computer Vision, Deep Learning and Remote Sensing applied to curvilinear detection on both optical and SAR satellite images (project abstract below). Both Geoazur and Inria Sophia Antipolis are ideally located in the heart of the French Riviera, inside the multi-cultural silicon valley of Europe (ie. Sophia-Antipolis, see https://en.wikipedia.org/wiki/Sophia_Antipolis).

This position is funded by University Côte d'Azur (UCA, see http://univ-cotedazur.fr/en#.XOforoWTpT4).

Duration: 18 months Starting date: between September 1st and December 1st 2019. Salary: gross salary per month 3000 EUR (ie. approximately 2400 EUR net)

Please see full announcement https://faultsrgems.oca.eu/images/FAULT/News/Post-doc_offer-AI-ManighettiZer..., or on https://euraxess.ec.europa.eu/jobs/411481

Candidate profile

Strong academic backgrounds in Stochastic Modeling, Deep Learning, Computer Vision, Remote Sensing and Parallel Programming with GPUs and/or multicore CPUs. A decent knowledge of Earth and telluric features (especially faults) will be appreciated.

To apply, please email a full application to both Isabelle Manighetti (manighetti@geoazur.unice.fr) and Josiane Zerubia (josiane.Zerubia@inria.fr), indicating “UCA-AI-post-doc” in the e-mail subject.

The application should contain:

- a motivation letter demonstrating motivation, academic strengths and related experience to this position. - CV including publication list - at least two major publications in pdf - minimum 2 reference letters

Project abstract

Curvilinear structure networks are widespread in both nature and anthropogenic systems, ranging from angiography, earth and environment sciences, to biology and anthropogenic activities. Recovering the existence and architecture of these curvilinear networks is an essential and fundamental task in all the related domains. At present, there has been an explosion of image data documenting these curvilinear structure networks. Therefore, it is of upmost importance to develop numerical approaches that may assist us efficiently to automatically extract curvilinear networks from image data.

In recent years, a bulk of works have been proposed to extract curvilinear networks. However, automated and high-quality curvilinear network extraction is still a challenging task nowadays. This is mainly due to the network shape complexity, low-contrast in images, and high annotation cost for training data. To address the problems aroused by these difficulties, this project intends to develop a novel, minimally-supervised curvilinear network extraction method by combining deep neural networks with active learning, where the deep neural networks are employed to automatically learn hierarchical and data-driven features of curvilinear networks, and the active learning is exploited to achieve high-quality extraction using as few annotations as possible. Furthermore, composite and hierarchical heuristic rules will be designed to constrain the geometry of curvilinear structures and guide the curvilinear graph growing.

The proposed approach will be tested and validated on extraction of tectonic fractures and faults from a dense collection of satellite and aerial data and “ground truth” available at the Géoazur laboratory in the framework of the Faults_R_Gems project co-funded by the University Côte d’Azur (UCA) and the French National Research Agency (ANR). Then we intend to apply the new automatic extraction approaches to other scenarios, as road extraction in remote sensing images of the Nice region, and blood vessel extraction in available medical image databases.