General information
Offer title : PhD Supervised classification of wood and charcoal from microscopic images (M/F) (H/F)
Reference : UMR7271-VIVROS-069
Number of position : 1
Workplace : VALBONNE
Date of publication : 18 September 2024
Type of Contract : PhD Student contract / Thesis offer
Contract Period : 36 months
Start date of the thesis : 1 February 2025
Proportion of work : Full time
Remuneration : 2 135,00 € gross monthly
Section(s) CN : Information sciences: bases of information technology, calculations, algorithms, representations, uses
Description of the thesis topic
This thesis will involve different steps:
- search for an optimal neural architecture taking into account different orthogonal sections of the same specimen,
- taking into account the hierarchical relationships between species, genus and families,
- study of the impact of taking into account the anatomical characteristics established by the IAWA
- determination of new anatomical characteristics
Work Context
Within the framework of the recently funded ANR project AI-WOOD, researchers at CEPAM, I3S and INRIA Université Côte d'Azur are collaborating to the development of new machine/deep learning approaches aiming at performing the taxonomical identification (i.e. classification at the species, genus or family level) of wood and charcoal from microscopic 2D images. The project has a main interest from an archaeological point of view, the main idea being to train a classifier on a modern collection (about 6000 images for 120 species) and then use it to identify ancient charcoals. The anthracologists (i.e. the archaeologists specialized in the identification and analysis of ancient charcoal) actually perform this identification relying on comparative anatomy and based on anatomical features settled by the IAWA that they build manually through microscopic observation. Apart from being long and tedious, this identification routine is not entirely satisfying, (also) due to the anatomical proximity of some essences.
Hence, the aim of this project is to explore the potential of machine/deep learning to directly identify the taxon of a specimen from the microscopic image and possibly to boost the identification routine. Although some attempts in this direction have been made in the literature (Rosa da Silva et al., 2022; Silva et al., 2022) there is still considerable room for improvement.
This PhD is part of the ANR AI-WOOD project on charcoal classification from microscopic images.
Deep Learning, Image Processing, Python programming (pytorch, keras).
Constraints and risks
None
Additional Information
Desired experience : Deep learning for images. Pratice in programming using python deep learning libraries such as pytorch, keras or tensorflow.