Tenure-Track Professor: Data-Driven PDEs & Uncertainty

Universität Wien; Österreich; Vor Ort; EUR 90.000 - 120.000; Vollzeit

Benefits dieser Stelle

Permanent position potential

Dynamic research environment

Support services

Zusammenfassung

Universität Wien invites applications for a tenure-track Professorship in Data Driven Partial Differential Equations within the Faculty of Mathematics. The successful candidate will complement ongoing research in applied and computational PDEs and contribute to teaching at bachelor, master and doctoral levels.

The role emphasizes development of numerical methods, data-driven techniques, and potential applications in biology, materials science, and astrophysics.

Qualifikationen

• Two years of international research experience during or after doctoral studies.
• Outstanding research achievements, excellent publication and funding record, international reputation.
• Gender and diversity competence.
• Experience in designing of and participating in research projects, ability to lead research groups and acquire third-party funding.
• Enthusiasm for excellent teaching and supervision at the bachelor's, master's, and doctoral level.

Aufgaben

• Complement the faculty’s ongoing research activities in applied and computational PDEs.
• Lead research groups and supervise doctoral, masters and bachelor projects.
• Collaborate across disciplines and contribute to teaching and supervision at all levels.

Kenntnisse

International research experience

Strong publication record

Funding acquisition

Leadership of research groups

Teaching and supervision

Data-driven PDE / numerical methods

Data science applications

Ausbildung

PhD in Mathematics or Applied Mathematics

Tools

Finite element software

PDE numerical methods

Jobbeschreibung

Universität Wien invites applications for a tenure-track Professorship in Data Driven Partial Differential Equations within the Faculty of Mathematics. The successful candidate will complement ongoing research in applied and computational PDEs and contribute to teaching at bachelor, master and doctoral levels.

The role emphasizes development of numerical methods, data-driven techniques, and potential applications in biology, materials science, and astrophysics.