ML Pipeline Engineer: Ramp-Up Estimation & Cooling Optimizer
Politecnico di Milano invites applications for a research fellowship in Engineering. The project focuses on upgrading machine learning based pipelines to estimate ramp-up duration in indoor environments, implementing a sliding window training scheme, and optimizing training window size.
Applicants will undergo an oral test to assess aptitude and English proficiency, and evaluation covers publications, theses, and prior research related to the fellowship.
#J-18808-Ljbffr
Applicants will undergo an oral test to assess aptitude and English proficiency, and evaluation covers publications, theses, and prior research related to the fellowship.
#J-18808-Ljbffr