Surrogate modeling and physics-informed learning for complex systems - 2026_IDR_DMAT_9
The research program aims to develop advanced methodologies at the interface between Numerical Analysis and Machine Learning, with a focus on physics-informed machine learning. The goal is to design learning strategies that incorporate the structure of the physical laws governing the system, enabling robust inferences even in the presence of sparse or uncertain data. In this context, the project will explore surrogate modeling approaches for the rapid approximation of solutions to complex differential problems. Particular attention will also be devoted to the optimization algorithms required for training these physics-informed machine and surrogate models. Project funded by Ministero dell’Università e della Ricerca in the context of FIS 2, SYNERGIZE, code FIS-2023-02228, CUP D53C24005440001.
Selection process
In order to participate in the selection, please read the call ("bando") available at the following website: https://www.polimi.it/en/bandi-incarichidiricerca
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 50 points) Relevance and pertinence of the publications, theses and scientific products attached to the research programme covered by the Fellowship (up to 15 points) Relevance and pertinence of previous research activities and work experience, if any, in relation to the research activity covered by the Fellowship (up to 15 points) Relevance and pertinence of their study programme to the research programme covered by the Fellowship (up to 20 points)