Post-doctoral project (2009-2010) in the Mét@risk Research Unit, Modeling and Statistics Team, Charanpal Dhanjal
This project aims at developing tools for the automatic regognition of a phenotype based on metabolomic data, the methodology is founded on recent concepts borrowed from the machine learning field (e.g. optimization of the ROC curve, «multi-class» ranking, functional data analysis).
Post-doctoral project (2006-2008) in the Mét@risk Research Unit, Modeling and Statistics Team, Anne-Laure Afchain
A new approach is proposed and new tools are being developed to study the emergence of a pathogenic bacterium in the food chain – the case of B. cereus in non-sterile products undergoing thermal treatment, in the context of a National Research Program on Food and Human Nutrition (Programme National de Recherches en Alimentation et Nutrition Humaine, PNRA) and National Research Agency (Agence Nationale de la Recherche, ANR) project.
Post-doctoral project (2007-2008) in the Mét@risk Research Unit, Modeling and Statistics Team, Mélanie Zetlaoui
The overall problem consists of assessing exposure to food-borne risks in individuals. In this context, this post-doctoral project focuses on breaking down the long-term weekly household purchasing data (SECODIP) available in order to predict individual levels of consumption. Statistical learning techniques (Lasso, Ridge, CART, SVM...) are applied to tackle this problem in a supervised approach: the decomposition phenomenon is learnt from another source of data providing individual levels of consumption for all family members, but only over a single week (INCA).
This post-doctoral project forms part of the ANR White Project TAMIS.
Writing: I. Albert, Met@risk's Unit
Creation date: 23 August 2008
Update: 15 June 2012