Research fellowships at SISSA mathLab, Trieste, Italy


Call #1 -  PRIN 2022 PNRR
Research title: "Development of machine learning algorithms for scientific computing and data analytics, and applications"
Duration of the research fellowship: 12 months
Gross remuneration per year:  € 24.867,00

Call #2 -  PNRR – iNEST
Research title: "Research title: "Scientific Computing, Model reduction, artificial intelligence or digital twins"
Duration of the research fellowship: 12 months
Gross remuneration per year:  € 24.336,00


Information for both applications

Deadline: The online application and submission procedure shall be completed by 24 July 2024, 11:59 p.m. CEST (Rome time). 
The reference letters shall be uploaded on the PICA platform by the candidate's referees/supervisors by 31 July 2024, 1:00 p.m. CEST (Rome time).

Fields of the research activity: Numerical analysis and simulation for the complex systems (industry, medicine, environment) held by PDEs and/or data-driven,
computational fluid dynamics for optimization and control, data science, automatic learning and uncertainty quantification, offline-online scientific computing, digital
twins, advanced programming, high-performance computing and/or large-scale computing competencies.

Scientist responsible for the research activity: Prof. Gianluigi Rozza
Members of the research group: Prof. Gianluigi Rozza (PI), Dr. Michele Girfoglio, Dr. Pasquale Africa, Dr. Niccolò Tonicello, and Dr. Federico Pichi. 
Collaboration SISSA MathLab: Prof. Antonio de Simone, Prof. Giovanni Noselli. Prof. Andrea Cangiani.

Requisites for the admission to the selection procedure:
- Master's degree in Mathematical Engineering, Biomedical Engineering, Industrial Engineering (Aerospace, Mechanical, Energy, Nuclear, etc), 
Physical Engineering, Civil Engineering or equiv., Mathematics, Physics, Computer Science, Computer Engineering, Data Science and Scientific Computing.
- Proven experience in mathematical modelling, numerical analysis and simulation for complex systems in biomedicine, environmental science and industry modelled by PDEs, optimisation and control, 
data science, automatic learning, data assimilation and uncertainty quantification, scientific computing programming, high-performance computing and/or large-scale computing competencies, as well as computer graphics.

Additional requirements, competencies, and abilities: 
Good knowledge of high-level programming languages, e.g., C/C++/Python; Scientific Computing software libraries, Reduced Order Modelling techniques (reduced basis methods, POD), efficient geometrical parametrisation techniques. 
Special interest in the technological aspect of offline-online scientific computing, as well as machine learning, digital twin, and artificial neural networks. 
A doctoral degree in Mathematics/Mathematical Engineering/Applied Mathematics/Numerical Analysis/Industrial Engineering, Computational Science and Engineering/Computer Science and/or a professional master in HPC and/or Data Science are preferred titles.

Notes: The submission of two reference letters is recommended. The letters shall be uploaded on the PICA portal by the referees/supervisors indicated by the candidate in the application.