Type
Stagiaire
Sujet
A sparse Bayesian infinite latent factor method for Near Infrared Spectrum data applied to wheat phenotype prediction
Date de début
Date de fin
Encadrant(s)
Guillaume KON KAM KING, Zacharie NAULET
Equipe(s)
Dynenvie
StatInfOmics
Année de soutenance (pour les thèses ou les stages)
2025
Ecole/université (pour les thèses et les stages)
University of Turin
Niveau/diplôme (pour les stages)
Master 2
Description/résumé
Objectives and goals of the internship:
- Develop predictive and interpretable models for Near-Infrared Spectroscopy (NIRS) data
- Implement numerical methods and algorithms for Bayesian analysis of NIRS data using a sparse Bayesian infinite latent factor model
- Analyse real-world wheat and maize datasets and build predictive models based on the analysis