Please find below the details for an open PhD position in computer
science/artificial intelligence in Lyon and Grenoble.
Multimodal merging, active perception, developmental learning, social
In the AMPLIFIER (Active Multisensory Perception & LearnIng For
InteractivE Robots) project, we study multimodal perception in humans
with psychophysics experiments and modeling to improve human robot
interactions for social robots. We adopt a constructivist and
sensori-motor approach which considers the perception as the result of
mastering sensori-motor contingencies that are learned incrementally all
life long [6, 7, 8]. We will study multiple research questions: 1- How
and when fusing data coming from various sensors? 2- How this merging
process evolves with age? 3- How active perception (i.e. active sampling
of data in the environment, e.g. by moving the eyes to sample visual
data) influences the multimodal perception (and reciprocally)? This
project (2018-2022) involves members from Lyon 1 University (LIRIS,
CRNL), Univ. Grenoble Alpes (LJK, Gipsa-lab, LPNC) and Hoomano, a
start-up located in Lyon.
Multi-sensory merging is a key feature to generate a consistent
perception of the environment . Moreover, it allows better detection
and discrimination of stimuli  and has been argued to be Bayes
optimal . In machine learning, multimodal merging is often performed
with classical methods developed for computer vision, natural language
processing, … Depending on the model, the fusion is done at dierent
stages of the processing chain . However, the impact of the fusion is
not well understood and usually is task dependent.
Instead of simply considering actions performed to achieve goals and
sustain a physical state (pragmatic action), active perception
proponents also consider actions performed to select or obtain more
information from the environment (epistemic action) [3, 4]. Active
perception through sensori-motor regularities helps to overcome the
complexity of sensory flows by focusing on the predictability of the
flows modification caused by the action  The PhD candidate will work within the AMPLIFIER team to test the
hypothesis that active perception is a key element in multimodal merging
(especially for weighting the relevant information in perception) and
how it can co-evolve with sensori-motor regularities learning. The
approach entails a computer science orientation, yet in strong
interaction with psychophysic/cognitive science and robotics. The
candidate will have to ground its developments in the neuroscience and
psychophysics literature, and contribute to the modeling of the
psychophysical data (probably via student supervision). His/Her main
objective will be to propose and implement a bio-inspired model for
active multisensory integration and learning (based on the previous work
of the advisors [5, 10] using neural fields, a dynamical model of
neuronal activity) that will be tested on a social robot toward the end
of the thesis.
Ideally, the candidate would have the following skills:
• background in artificial intelligence / cognitive science /
computational neuroscience / machine learning (or equivalent)
• good programming skills (especially in Python)
• interest in neuroscience and/or psychophysics
• ability to work in a multi-disciplinary and multi-site team
• previous experience in a scientific environment
• good reporting/documentation skills
• good written/oral English skills
Any of these skills will be a plus:
• programming skills in web technologies
• previous experience with robots (especially Nao and Pepper)
• previous experience with machine learning/artificial intelligence
• previous experience in neuroscience and/or psychophysics
18 months at LIRIS laboratory, Lyon, France and 18 months at LJK
laboratory, Grenoble, France. The precise split between the two
locations is not fixed and can be negotiated.
The student will also have to work at the Hoomano office in Lyon for the
3 years (standard PhD duration in France) with an ideal starting date in
September/October 2018 (can be negotiated).
Around 1685€/month gross salary. Funding is guaranteed for 3 years
(obtained on a regional call – ARC AuRA). Additionally to his research,
the candidate can also give lessons at universities in Lyon and/or
Grenoble with additional remuneration.
• Mathieu Lefort: associate professor at SMA group, LIRIS laboratory, Lyon
• Jean-Charles Quinton: associate professor at SVH team, LJK laboratory,
To apply, please send a CV and application letter to Mathieu Lefort
(email@example.com) and Jean-Charles Quinton
(firstname.lastname@example.org). Candidates can apply until the 17th of
June. Interviews will be done the week after, aiming a final decision
before the end of June.
If you have any question regarding this position, please send an email
to Mathieu Lefort.
 Pradeep K Atrey, M Anwar Hossain, Abdulmotaleb El Saddik, and Mohan
S Kankanhalli. Multimodal fusion for multimedia analysis: a survey.
Multimedia systems , 16(6):345-379, 2010.
 Marc O Ernst and Martin S Banks. Humans integrate visual and haptic
information in a statistically optimal fashion. Nature ,
 Karl Friston, Jérémie Mattout, and James Kilner. Action
understanding and active inference. Biological cybernetics ,
 David Kirsh and Paul Maglio. On distinguishing epistemic from
pragmatic action. Cognitive science , 18(4):513-549, 1994.
 Mathieu Lefort, Yann Boniface, and Bernard Girau. Somma: Cortically
inspired paradigms for multimodal processing. In IJCNN 2013 , pages 1-8.
 Matteo Mossio and Dario Taraborelli. Action-dependent perceptual
invariants: From ecological to sensorimotor approaches. Consciousness
and cognition , 17(4):1324-1340, 2008.
 J Kevin O’Regan and Alva Noë. A sensorimotor account of vision and
visual consciousness. Behavioral and brain sciences , 24(5):939-973, 2001.
 Jean Piaget. La naissance de l’intelligence chez l’enfant, volume
370. Delachaux et Niestlé Neufchâtel, Switzerland, 1977.
 Barry E Stein and M Alex Meredith. The merging of the senses. The
MIT Press, 1993.
 Nicola Catenacci Volpi, Jean Charles Quinton, and Giovanni Pezzulo.
How active perception and attractor dynamics shape perceptual
categorization: A computational model. Neural Networks, 60:1-16, 2014.
 Robert B Welch. Intersensory interactions. Handbook of perception
and human performance. Sensory processes and perception, 1986.
Desinscription: envoyez un message a: email@example.com
Pour obtenir de l’aide, ecrivez a: firstname.lastname@example.org