Inria（法国国家信息与自动化研究所）旗下Morphology & Images实验室招博士生
实验室主页：
https://team.inria.fr/morpheme/
Joint team between Inria, CNRS and Université Côte d’Azur (UCA), affiliated with Inria SAM, Computer Science+Signals+Systems Laboratory (I3S) and Institute of Biology Valrose (iBV)
History: Creation process started in 2010; Status of « Equipe-Projet Commune (EPC) » in 2013; Renewal in 2017
Positioning: Be at the interface between computational science and biology
Motivation: The morphology and topology of mesoscopic structures have a key influence on the functional behavior of organs
Objectives: Characterize and model the morphological & topological properties, and the development of biological structures
Framework:
Scales: from cell to supracellular scale
Modalities: various microscopy systems (confocal, 2-photon, phase-contrast, video, micro-tomography)
Data: in vivo images in 2D, 2D+t, 3D or 3D+t
Tools: image processing, statistical learning and computational modeling
In the long term: Allow for a better understanding of the development of normal tissues and a characterization at the supracellular level of pathologies such as the Fragile X syndrome, Alzheimer or diabetes.
Image acquisition. Includes:
For a given biological question, definition of studied phenomena (experimental conditions) and preparation of samples
Optimization of the acquisition protocol (staining, imaging…) and definition of relevant quantitative characteristics
Reconstruction/restoration of native data to improve the image readability and interpretation
Structure extractionDetection and delineation of the biological structures of interest in images, which includes the use of previously defined models for improving the detection. Two main challenges are the variability of biological structures and the huge size of datasets
Interpretation/Classification. Includes:
Inference of parameters associated with the model that has been used to extract the biological structure under study
Definition of classification schemes for characterizing the different populations based either on the model parameters or on some specific metric between the extracted structures. The aim is to provide biological information characterizing the different populations
Modeling. Back-and-forth approach:
Forth approach: modeling biological phenomena such as axon growth or network topology in different contexts using image-based information to calibrate/validate the models
Back approach: using a prior model to extract relevant information from images
说明：由于疫情原因，经费紧张，需要申请CSC（China Scholarship Council，即国家留学基金）。
申请邮箱：rudan.xiao@inria.fr
邮件主题：52CV推荐
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