Mots-Clés

Biostatistique, bioinformatique, Science des aliments, analyse de donnée, gaspillage alimentaire

Description

Position Summary
Households account for 53% of food waste in Europe, more than any other actors in the food chain. Fresh fruits and vegetables (F&V) are known to constitute the highest volumes of wasted food mainly associated with microbial contamination and spoilage. In this context, the ANR FOODREST project, involving both the LEGO and LUBEM teams of the Université de Bretagne Occidentale, aims at studying the stage of the food chain which represents the largest volumes of food waste: the consumer. Its goal is to help reduce the food waste of fresh fruit and vegetables (F&V) by selecting and adapting hygiene and conservation recommendations that are effective both microbiologically and in terms of changing current practices. The originality of this ANR project is based on the complementarity of a microbiological approach (studied by the LUBEM laboratory), to better understand spoilage processes according to storage locations and to explore the efficiency of hygiene recommendations, and a behavioral approach (studied by the LEGO laboratory), in order to capture household perceptions and practices in relation to the F&V spoilage.

The post-doc will have three main tasks, for this 11-month position:
• carry out a meta-analysis to identify and quantify the effect of different factors on the spoilage of fresh fruit and vegetables;
• develop statistical models (multivariate analysis, group data, multi-block modelling, etc.) to analyse the data generated during the project (microbiota, impact of storage or consumption practices) and predict the occurrence of spoilage and waste;
• contribute to the dissemination of results (project meetings, conferences, publications).

Post-doctoral candidate qualification and profile:
• solid knowledge in statistics;
• ability to work cooperatively in an interdisciplinary group;
• ability to communicate scientific results in French and English;
• interest for application of statistical methods to biology.