Machine learning models and computational geometry approaches for material modeling and 4D printing

Position: Research appointment (pre-doc) Institute: Uni. Pavia
New! Posted on: 10/02/2026 Deadline: 12/03/2026

Scientific-Disciplinary Group

01/MATH-05 - Numerical Analysis, 08/CEAR-06 - Mechanics Of Solids And Structures

Description

The goal of this research program is three-fold: (i) development of machine learning models to predict and optimize the mechanical behavior of soft materials (such as polymers); (ii) integration of methods of computational geometry; (iii) extension of these models and approaches to soft materials for 4D printing. Activities ill consists of boththeoretical studies and numerical implementation.

Funding body

Università di Pavia

Selection process

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The application for admission to the selection procedure (dated and signed),the documents and publications considered useful for the evaluation must be submitted, under penalty of exclusion, electronically, by visiting the following web page: https://pica.cineca.it/unipv/tipologia/idr selecting the Competition Notice you have chosen. In order to register with the system, you must have an email address. Following the guidelines published in the procedure, the applicant must enter all the information required for the application, enclosing all documents in electronic .PDF format. The selection of candidates is carried out on the basis of qualifications and possible interview (with test of knowledge of the foreign language) (see Art. 1 par. 1 Call for applications). The public call with detailed attendance and selection rules (Art 4 and 6) can be found at: https://pica.cineca.it/unipv/tipologia/idrThe selections are open to candidates, Italian or foreigner, in possession, on the date of the deadline for submitting applications for the admission to the selection, of a master's degree or single-cycle degree obtained no more than six years previously are eligible to participate in the selection process. These degrees must be relevant to the subject of the research activity. Further specific assessable qualifications (see Art. 1 par. 1 Call for applications):- PhD degree in Mathematics or Applied Mathematics (already obtained at the call deadline or to be obtained before 6 months from call deadline)- Proven experience on theoretical notions and numerical treatment of partial differential equations- Proven experience on programming (for instance, Matlab, C++, Python)- Proven experience on notions of differential geometry, conformal geometry, and quasi- conformal geometry- Proven experience on machine learningapproaches