In the MERLE (Multimodal Effective Representation Learning of Evolution of
birds) project, we are interested in the learning of visual and multimodal
representation that can help biologists in the phylogenetic classification
and modeling evolution of birds. Currently paleontologists use ad hoc
features of bird appearance (color, feather, bones, …) to model evolution
and classify clades. We want to explore how deep learning architectures can
help to automatically extract relevant features for these biological
investigations. We are currently recruiting two master trainees with the
following planned missions:
- data pre-processing (e.g. based on the ‘birds of the world’ database (for
biologists)) - study of representation learning architectures (VAE or equivalent)
regarding the features extracted and how to constraint them - fusion of multiple modalities (sound and image)
The ideal candidate will have a master’s degree in artificial
intelligence/machine learning (or equivalent), previous experience in deep
learning, good teamwork and interest in multidisciplinary research.
The internships will start in February 2023 (flexible date) for 5-6 months
in LIRIS and LGL laboratories (Lyon, France). The gratification is around
550€/month.
To apply, please send a mail to mathieu.lefort@liris.cnrs.fr and
stefan.duffner@liris.cnrs.fr with your CV, cover letter, academic
transcript and any other document you deem relevant.