Société Française de Bioinformatique

Mots-Clés

Computational Network Biology, Machine Learning

Description

The research carried out will leverage and support the I3-BioNet framework, which combines inference, interrogation and intervention with co-regulatory networks. We combined ML (for data analysis, model formation, experimental design), with network biology (bioinformatics, systems biology modelling) and  visualization  to execute a cycle of systems biology model development. A second  challenge is to facilitate the collaboration  with scientists to accelerate systems biology models development. AI systems have super-human scientific powers: they can learn from vast amounts of data, execute error-free logical reasoning, execute near optimal probabilistic reasoning, coordinate the parallel testing of different hypotheses; and only scientists have the knowledge and experience to direct these experiments into profitable areas of research.

Qualifications: candidates should have a background or strong interest in computational network biology and  machine learning and solid programming skills (R/Python)