Post-doctorate position : Multi-scale Bayesian prediction in the presence of categorical and hidden variables – application to the prediction of energy consumption in buildings

This project is part of the chair MIAI / BALTEEC (Bayesian Active Learning Techniques for Energy Efficient buildings) that involves 3 universities (Clermont-Ferrand, Grenoble, Chambery), private companies (URBS, Heliocity) and IFPEN

Planned work : the work planned is primarily in the field of applied mathematics and computational statistics and concerns design of experiments, Bayesian models and kernel methods (Gaussian process regression) for classification and/or regression (Rasmussen and Williams 2006, Steinwart and Christmann 2008). It is secondarily a work in energy consumption at the housing or building scale.

Profile of candidate : PhD in applied mathematics (probability / statistics / optimization) or PhD in engineering with a strong taste for statistical models (typically Bayesian models, Gaussian process Regression), motivation to work on environmental-related issues in particular energy in buildings

Duration, starting date : 1 year, can start in March 2026 or later until the position is filled

Location : LIMOS laboratory in Clermont-Ferrand or Saint-Etienne, France. Possibilities to be located in the Grenoble area (to discuss).

Salary : up to 3700 euros (before taxes, “brut”) = 2900 euros (after taxes, “net”), depending on experience

Contacts : Rodolphe Le Riche (rodolphe.le_riche@uca.fr), Marc Grossouvre (marcgrossouvre@urbs.fr), Clémentine Prieur (co-chair BALTEEC, clementine.prieur@univ-grenoble-alpes.fr)

More informations here