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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.
Submission deadline June 30th, 2019
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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 November 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-ManighettiZerubia.pdf,
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.
phone: +33 4 92 38 78 65, fax: +33 4 92 38 78 58
email: Josiane.Zerubia@inria.fr
web: http://www-sop.inria.fr/members/Josiane.Zerubia/index-eng.html
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Josiane Zerubia
INRIA Sophia-Antipolis Méditerranée
BP 93, 2004 Route des Lucioles
06902 Sophia-Antipolis Cedex - France