Scientific machine learning for the numerical simulation of complex systems - 2026_IDR_DMAT_2
Posted on: 27/02/2026
Deadline: 31/03/2026
Scientific-Disciplinary Group
01/MATH-05 - Numerical Analysis
Description
The research project focuses on the analysis and implementation of scientific machine learning techniques for building efficient reduced-order models in the context of the numerical simulation of parameterized partial differential equations. The analysis of recent deep learning paradigms (such as self-attention mechanisms) in the context of reduced-order models, as well as their application to areas of interest in engineering like, e.g., computational fluid dynamics, are key activities of this research program. FIS Starting Grant "DREAM" (FIS00003154), CUP: D53C23003180001
Job posting website
Number of positions
1
Funding body
Politecnico di Milano
Selection process
Click to expand
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 40 points) Relevance and pertinence of the publications, theses and scientific products attached to the research programme covered by the Fellowship (up to 20 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 20 points) Relevance and pertinence of their study programme to the research programme covered by the Fellowship (up to 20 points)
View the original posting on the MUR website: Go to MUR website