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2-year post doc position in deep learning at Saint-Etienne

Country : France

Website : http://univ-st-etienne.fr

Description

Semi-supervised anomaly detection in ski lift videos via active learning
Context
Safety in ski lifts is a major concern for ski resort operators. To
prevent possible accidents, it is necessary to detect dangerous
situations on ski lifts as early (after boarding) as possible. The main
goal of the **MIVAO project** is to develop a **machine learning and
computer vision** system able to do real-time analysis of videos
acquired by a camera fixed on one of the first pillar of the chair lift,
and trigger an alarm when a problem (safety bar not in place, child
alone on the chair, two many persons, child sliding under the safety
bar, etc.) is detected. This cutting edge project is led by the BlueCime
company (located in Grenoble, France) in partnership with the Hubert
Curien Laboratory (Saint-Etienne, France), the Gipsa Lab (Grenoble) and
an increasing number of ski resorts in the Alpes mountains.
The Post-doc candidate will join the working team of Mivao composed of
three PhD students and more than 10 researchers and engineers. The
candidate will be hosted in the **Hubert Curien Laboratory** in
Saint-Étienne, working with and contributing to the supervision of 2
Ph.D. students.
Scientific objectives
The objective of this project is to propose new machine learning methods
to analyze the acquired videos and trigger an alarm when an anomaly is
detected. These methods will be based on deep convolutional networks
which have shown outstanding performance on related applications but
also on this specific dataset, in a supervised setting. Because the
system can continuously acquire videos and because labeling those videos
is expensive, the post doc will mainly focus on unsupervised,
semi-supervised and active learning methods. The main issues to address
will be:
1. Study, implement and test unsupervised methods in deep learning for
anomaly detection
2. Propose new unsupervised and semi-supervised methods for anomaly
detection
3. Propose new active learning methods with domain adaptation, to
benefit from a few well-labeled examples to improve an
existing system.
Required skills
We are searching for an outstandi*ng and highly motivated candidate*
with:
- A PhD degree in machine learning with preferably some knowledge in
computer vision and deep learning.
- A list of excellent publications (at reference journals and
conferences in machine learning, pattern mining and/or
computer vision) that demonstrates the expertise of the candidate
- Very strong programming skills in languages such as Python (and,
ideally, an experience in Tensorflow). The candidate will need to
show during the interview, examples of codes that
he/she implemented.
Working environment
Saint-Etienne is a mid-size city (14th biggest city in France) and one
of the cheapest in terms of living cost (accommodation, food). It is 70
km from Lyon (45 min by train) and in a middle of a splendid regional
park where skiing, hiking, climbing, biking are possible. The hosting
research group has established expertise in relevant domains including
statistical machine learning, transfer learning, unsupervised learning
and computer vision. The successful candidate will have the opportunity
to work in synergy with two Ph.D. students. The **expected salary is
2193 euros NET per month** (gross salary: 2727 euros).
Application instructions
The application consists of a motivation letter, CV (with a detailed
list of publications and links to e.g implementations on Github), names
and contact details of two references. Applications should be submitted
before *June 30th* via electronic mail to *the contacts below.*
Contact
Elisa Fromont,
[*elisa.fromont-AT-univ-st-etienne.fr*]
Phone: (+33) 4 77 91 57 67
Rémi Emonet,
[*remi.emonet-AT-univ-st-etienne.fr*]
Phone: (+33) 4 77 91 57 23

Last modified: 2017-06-14 23:53:30