Efficient numerical methods for Partial Differential Equations

Italian Ministry of Education, University and Research; Italia; On-site; EUR 30,000 - 45,000; Full time

Job summary

Sapienza University of Rome, Department of Mathematics, invites applications for a research position focused on numerical analysis and mathematical modelling.

The role emphasises development and analysis of efficient numerical methods for ordinary and partial differential equations, with potential work on model order reduction, optimal control for dynamical systems, and data-driven approaches including PINNs.

Qualifications

• PhD in mathematics or a closely related field.
• Experience in numerical analysis of ODE/PDEs.
• Strong background in modeling and computational methods.
• Ability to carry out independent research and collaboration.

Responsibilities

• Conduct research in numerical analysis and mathematical modelling for ODEs/PDEs.
• Develop and analyse efficient numerical methods for equations and systems.
• Explore model order reduction, optimal control, and data-driven approaches including PINNs.
• Contribute to dissemination of results and collaboration within the department.

Skills

Numerical analysis

Mathematical modelling

Model order reduction

Optimal control

Data-driven methods

Machine learning

PINNs

Education

PhD in Mathematics or closely related field

Job description

Organisation/Company Dipartimento di Matematica - Sapienza Università di Roma Research Field Mathematics » Computational mathematics Researcher Profile Recognised Researcher (R2) Leading Researcher (R4) First Stage Researcher (R1) Established Researcher (R3) Application Deadline 3 Dec 2026 - 23:59 (UTC) Country Italy Type of Contract To be defined Job Status Not Applicable Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

The research position focuses on numerical analysis and mathematical modeling, with particular emphasis on the development and analysis of efficient numerical methods for ordinary and partial differential equations. Research activities may include model order reduction techniques, optimal control methods for dynamical systems, and data-driven approaches for the identification and approximation of mathematical models, including machine learning techniques and Physics-Informed Neural Networks (PINNs). The research will encompass theoretical, algorithmic, and computational aspects, aiming to develop accurate and efficient methodologies for the simulation, analysis, and control of complex dynamical systems.

Additional Information

Eligibility criteria

• Italy

Eligibility of fellows: country/ies of residence:

• OTHER

Eligibility of fellows: nationality/ies: