prof.dr.ing. B. Rosic (Bojana)

Bojana Rosic is head of the Applied Mechanics & Data Analysis (previously known as Structural Dynamics, Acoustics & Control) group, please see AMDA. She studied Mechanical Engineering with specialization in Applied Mechanics and Automatic Control at the Faculty of Technical Sciences (prev. Faculty of Mechanical Engineering) in Kragujevac, Serbia. After completing her diploma studies, she pursued further academic specialization in Nonlinear Mechanics (2-year postgraduate study comparable to PDeng in research) at the Faculty of Technical Sciences in Kragujevac, Serbia. During PhD time, her research interests have grown towards combination of Computer Science and Mechanical Engineering. Hence, she completed dual degree PhD programme in Applied Mathematics (with the focus on stochastic modelling and uncertainty quantification) at the Carl-Friedrich-Gauß-Faculty (Mathematics and Computer Science), Technische Universitat Braunschweig and at the Faculty of Technical Sciences in Kragujevac, Serbia. Her PhD thesis was awarded by German Association for Computational Mechanics (GACM) as the best PhD thesis in Germany. The PhD results have also lead to recognition of German Association for Applied Mathematics and Mechanics (GAMM) which entitled her as GAMM Junior fellow for duration of three years. Bojana joined University of Twente in May 2019 as full professor. She is a Mare Balticum fellow of University of Rostock in Germany, and is engaged in research planning of Low Energy Data Centers and Twente Center for Advanced Battery, University of Twente. In addition she is also scientific board member of the Fraunhofer Project Centre (FPC) (link), UT, as well as AI board member of Digital Society Institute (DSI). In addition, Bojana is also board member of Examination Board of ME and SET programmes.

Her research interest lies in an interplay of nonlinear mechanics/structural dynamics modelling and the development of machine learning techniques with the focus on the predictive modelling of systems/processes/materials, and their assimilation with the measurement data (Digital Twin/AI). In particular, the focus is on the stochastic modelling of systems/processes/materials and their control (Uncertainty quantification/Bayesian learning/Model predictive Control and Reinforcement learning, Generative Design).

Faculty of Engineering Technology (ET), Applied Mechanics & Data Analysis (AMDA)

Stochastic description of materials and systems
Uncertainty quantification and propagation
Data assimilation and statistical inference
Reduced modelling
Machine learning and data analysis
Multiscale analysis

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