Upgrade and Optimization of Machine learning-based pipelines for Ramp-up estimation and enhancement of recommendation engine - Rendita Project, ID:MI_DDR_00114, CUP: F53C25001760001 - 2026_IDR_DENG_33

id="offer-description" class="ecl-u-type-heading-2 ecl-u-mt-none ecl-u-mb-l">Offer Description The research program involves the upgrade of machine learning-based pipeline, which are aimed at the estimation of ramp-up duration in indoor environments, implementing sliding window-training scheme, along with the optimization of the size of the training window. The latter task also includes introducing a safety margin considering the offered estimation accuracy of the developed pipelines. The research program also involves implementation and improvement of a recommendation engine aimed at suggesting the optimal hours of the the operation of the centralized cooling systems (e.g. chiller) and that of supply units (e.g. fan-coils) in individual indoor spaces. Where to apply Website https://aunicalogin.polimi.it/aunicalogin/getservizio.xml?id_servizio=1079 . Requirements. Additional Information . Eligibility criteria. Oral test aimed at ascertaining candidates’ aptitude and suitability to carry out the research activity covered by the Fellowship, as well as at assessing their knowledge of English and/or other languages relevant to the research activities to be performed (up to 40 points). Relevance and pertinence of the publications, theses and scientific products attached to the research programme covered by the Fellowship (up to 10 points). Relevance and pertinence of previous research activities and work experience, if any, in relation to the research activity covered by the Fello