Assistant professor position on Exploratory data analytics
CY Cergy Paris University ( is recruiting an assistant professor starting in Fall 2021. The position is in the ETIS research lab ( for the research activity and at the IUT (University Institute of Technology) for teaching.

In research, the successful candidate will join the ETIS Laboratory (UMR 8051) and will integrate the MIDI team, whose researchers have experience in structured and semi-structured big data management and analytics (including multimedia indexing and classification), in interaction with different machine learning techniques for analyzing various types of data.

We are interested in adding complementary expertise for the team in the area of Exploratory Data Analytics that would bring together techniques of data analysis (e.g. mining/learning of patterns from the data, detect outliers or data anomalies in general, uncover the underlying structure of the data, extract previously unknown knowledge from data, discover sparsity in the data, etc.) and techniques of data visualization (including algorithms for graph visualizations, visualization of data summaries, techniques for visualizing outliers or exceptions, etc.) in order to contribute into the testing of initial data hypothesis, checking assumptions on unknown datasets, determining various parameters from the data and finally developing (based on the previous) robust Machine Learning and Artificial Intelligence models for data analytics.

The full profile description is available on the Galaxie French official site for academic recruitment, see 

Keywords: Exploratory data analysis, intelligent data visualization, knowledge extraction, pattern learning, data anomalies, machine learning, artificial intelligence

Please contact Dan Vodislav ( asap if you are interested in applying for this position. The recruitment procedure uses the Galaxie French official site, with applications open until March 30, 2021, but an early contact is encouraged if you are not familiar with the French recruitment process.