[MARMAM] Postdoc - Machine Learning for the re-identification of St. Lawrence Belugas

Clément Chion clementchion at gmail.com
Mon Jul 6 07:07:23 PDT 2020


Dear Marmamers,


We are now recruiting a postdoc in Machine Learning for beluga
re-identification in the St. Lawrence Estuary. The selected candidate will
have the opportunity to work on a huge database of more than 30 years of
photo-ID! All details below.


Do not hesitate to contact me if you have any questions.


Best regards,


Clément


Prof. Clément Chion <http://www.researchgate.net/profile/Clement_Chion/>,
PhD
Université du Québec en Outaouais (UQO)
Département des sciences naturelles
ISFORT <http://isfort.uqo.ca/nos_etudiants/clement-chion>
819-595-3900 poste: 1467
819-503-2539



*Project title*

Machine learning approach for the automatic re-identification of
individuals of the St. Lawrence Estuary Beluga whales population using
photo-identification data.



*Context and Objective*

Re-identifying animals in the wild using photo-identification (photo-ID)
allows to determine key ecological parameters of wildlife populations (
*e.g.*, abundance, carrying capacity, community structure). For over 31
years, the Group for Research and Education on Marine Mammals (GREMM) has
built and maintained a photo-ID database for the population of the St.
Lawrence Estuary Beluga (SLEB) taken from a research boat. Those shots of
belugas’ flanks allowed to identify about 350 individuals among the whole
population, which counts around 1000 individuals. The traditional
re-identification process involves the examination of each photo by an
operator to locate any distinctive marks allowing to either identify a
known individual or add a new one to the database. This task is a very
tedious task, and in the past 31 years, only 21 years of photo-ID have been
partially processed. Moreover, more photos are taken every year than it is
possible to process manually.



However, those data are of major importance to characterize the social
dynamics of the SLEB population. Indeed, the recognition of individuals
making different herds is the key to identify distinct communities, which
each have their own pattern of use of the summer habitat and site-fidelity.
Therefore, the vulnerability of each SLEB community with regard to human
activities (*e.g.*, navigation) depends on spatiotemporal patterns of
habitat use that must be characterized to the best of our knowledge. In
this context, the objective of the project is to investigate a machine
learning approach (e.g., based on Siamese neural  networks), but also the
existing relevant literature from the privacy domain, for the automatic
re-identification of known individuals from the beluga population but also
to discover new individuals that were unregistered until then. The
automatization of this process will notably enable the reduction of the
biases introduced by the human judgement for the recognition of the
individuals with distinctive marks.







The postdoctoral candidate must have the following qualities: motivation,
curiosity, sense of initiative, autonomy, creativity as well as
demonstrating excellent capacities to work as part of a team. He/she will
be encouraged to travel to present the results to international scientific
conferences, in addition to the meeting with partners and relevant
stakeholders. The selected candidate will also contribute to the production
and writing of project deliverables.



*Background and skills *

●      PhD in Computer Science and/or recognized experience in the
development of unsupervised and supervised machine learning algorithms

●      Experience in technology transfer

●      Capacity to write grant and funding demands

●      Ability to communicate (oral and written) scientific results to
experts and non-experts, including the writing of scientific papers and the
review of the state-of-the-art in English

*Assets*

●      Experience in Siamese neural networks

●      Experience in deep learning and/or in anonymisation/re-identification

●      Proficiency in spoken French



*Period*

●      Start: Now

●      End: March 31, 2022



*Rémunération*

●      50 k$/year + funding available for conferences



*Location*

●      Gatineau, Montréal or Ripon (Québec, Canada)



*Application procedure*

●      Send by email a CV (academic format), cover letter outlining your
motivation along with your skills and assets with regard to the project,
and the name and contact information of three referees from Academia :

○      Pr Clément Chion (clement.chion at uqo.ca)

○      Pr Sébastien Gambs (gambs.sebastien at uqam.ca)



●      *Deadline* : July 31, 2020, or until the position is filled
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