Technische Universität Dresden (TUD), as a University of Excellence, is one of the leading and most dynamic research institutions in the country. Founded in 1828, today it is a globally oriented, regionally anchored top university as it focuses on the grand challenges of the 21st century. lt develops innovative solutions for the world’s most pressing issues. In research and academic programs, the university unites the natural and engineering sciences with the humanities, social sciences and medicine. This wide range of disciplines is a special feature, facilitating interdisciplinarity and transfer of science to society. As a modern employer, it offers attractive working conditions to all employees in teaching, research, technology and administration. The goal is to promote and develop their individual abilities while empowering everyone to reach their full potential. TUD embodies a university culture that is characterized by cosmopolitanism, mutual appreciation, thriving innovation and active participation. For TUD diversity is an essential feature and a quality criterion of an excellent university. Accordingly, we welcome all applicants who would like to commit themselves, their achievements and productivity to the success of the whole institution.

The Research Training Group “Mineral-bonded composites for enhanced structural impact safety” (GRK 2250), funded by the German Research Foundation (DFG), is offering twelve positions starting from May 1, 2023, subject to resources being available, as

Research Associate / PhD student (m/f/x)

(Subject to personal qualification employees are remunerated according to salary group E 13 TV-L)

limited until April 30, 2026. The period of employment is governed by the Fixed Term Research Contracts Act (Wissenschaftszeitvertragsgesetz – WissZeitVG). The position aims at obtaining further academic qualification (e.g. PhD).

For further information see the attachment.

GRK 2250 Call for 3rd Cohort

Data-driven computing is becoming a new paradigm in several scientific fields with a tremendous impact on new technologies. In solid mechanics, the availability of large volumes of data through modern experimental techniques is enabling machine learning methods to open new perspectives in material modelling. In the framework of the research activities of Spoke 7 “Materials & Molecular Sciences” of the National Centre for HPC, Big Data and Quantum Computing, the present PhD project will be focused on the development and implementation of unsupervised data-driven techniques to automatically discover constitutive laws hidden in large volumes of data. Unsupervised approaches require no stress data. Therefore, the methods will have to rely only on full-field displacements and global reaction forces data. The project aims at developing these approaches for materials with complex behaviour, such as rate-dependent, shape memory, temperature-responsive, etc.

Additional information in the enclosed attachment or in the website: https://www.unifi.it/p12246.html

For additional information, please contact
– Enzo Marino (enzo.marino@unifi.it) for scientific related questions
– INDICEE Secretariat (dott-dicea@unifi.it) for administrative issues