MANIGHETTI Isabelle and ZERUBIA Josiane
2019-06-16 08:10:08
Institution: GEOAZUR & INRIA (Sophia Antipolis, Nice region, France)
Open Until: 2019-07-31
Project abstract
The 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 and blood vessel extraction in available medical image databases.
Candidate profile
Strong academic backgrounds in Stochastic Modeling, Deep Learning, Computer Vision, Remote Sensing and Parallel Programming. A decent knowledge of Earth and telluric features (especially faults) will be appreciated.
At UCA, Géoazur and Inria we seek to increase the number of women in areas where they are under-represented and therefore we explicitly encourage women to apply. We are also committed to increasing the number of individuals with disabilities in our workforce and therefore we encourage applications from such qualified individuals.
Post-doc salary and conditions
Duration: 18 months
Starting date: between September 1st and December 1st, 2019
Salary: gross salary per month 3000 EUR (i.e. approx. 2400 EUR net)
Hosting laboratory: GEOAZUR, Sophia Antipolis (https://geoazur.oca.eu/fr/acc-geoazur)
Advisors: Drs. Isabelle MANIGHETTI (Géoazur) and Josiane ZERUBIA (Inria-SAM). See:
https://www.oca.eu/fr/isabelle-manighetti
http://www-sop.inria.fr/members/Josiane.Zerubia/index-eng.html
Work conditions: Tight collaboration between Géoazur and Inria-SAM (http://www-sop.inria.fr). Position located at Géoazur, with research discussions planned twice a week at Inria Sophia.
How to apply
Please email a full application to both Isabelle Manighetti (manighetti<at>geoazur.unice.fr) and Josiane Zerubia (josiane.zerubia<at>inria.fr), indicating “UCA-AI-post-doc” in the e-mail subject line. The application should contain:
- a motivation letter demonstrating motivation, academic strengths, and related experience to the position
- CV including publication list
- at least two major publications in pdf
- minimum 2 reference letters
Open Until: 2019-07-31
Project abstract
The 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 and blood vessel extraction in available medical image databases.
Candidate profile
Strong academic backgrounds in Stochastic Modeling, Deep Learning, Computer Vision, Remote Sensing and Parallel Programming. A decent knowledge of Earth and telluric features (especially faults) will be appreciated.
At UCA, Géoazur and Inria we seek to increase the number of women in areas where they are under-represented and therefore we explicitly encourage women to apply. We are also committed to increasing the number of individuals with disabilities in our workforce and therefore we encourage applications from such qualified individuals.
Post-doc salary and conditions
Duration: 18 months
Starting date: between September 1st and December 1st, 2019
Salary: gross salary per month 3000 EUR (i.e. approx. 2400 EUR net)
Hosting laboratory: GEOAZUR, Sophia Antipolis (https://geoazur.oca.eu/fr/acc-geoazur)
Advisors: Drs. Isabelle MANIGHETTI (Géoazur) and Josiane ZERUBIA (Inria-SAM). See:
https://www.oca.eu/fr/isabelle-manighetti
http://www-sop.inria.fr/members/Josiane.Zerubia/index-eng.html
Work conditions: Tight collaboration between Géoazur and Inria-SAM (http://www-sop.inria.fr). Position located at Géoazur, with research discussions planned twice a week at Inria Sophia.
How to apply
Please email a full application to both Isabelle Manighetti (manighetti<at>geoazur.unice.fr) and Josiane Zerubia (josiane.zerubia<at>inria.fr), indicating “UCA-AI-post-doc” in the e-mail subject line. The application should contain:
- a motivation letter demonstrating motivation, academic strengths, and related experience to the position
- CV including publication list
- at least two major publications in pdf
- minimum 2 reference letters
