subject to third party funding for up to five years. The research is connected to the project “Deep Turb – Deep Learning in and of Turbulence”, which is funded by the Carl Zeiss Foundation for a period of 5 years. The project aims at conducting interdisciplinary research on large-scale structure evolution, experimental and numerical data analysis, data-driven modelling, and prediction of the dynamics in turbulent convection flows. This research is motivated by the demand to model processes that remain typically unresolved and have to be parametrized, e.g., in large-scale atmospheric turbulence or technological heat transfer processes. In the focus of the research is the development and application of machine learning techniques such as reservoir computing or deep neural networks as well as an improvement of our understanding of their mathematical foundations. The project brings together researchers from mathematics, computer science and fluid mechanics in an interdisciplinary research group.
1. Fluid Mechanics – Direct Numerical Simulation (DNS) of turbulent large-aspect-ratio Rayleigh-Bénard convection and data-driven modelling2. Fluid Mechanics – Experimental long-time measurements of velocity and temperature in Rayleigh-Bénard convection in gases and liquids applying optical imaging (PIV/LIF/PIT)3. Machine Learning – Development and training of (recurrent) neural networks and their application to numerical and experimental analysis and prediction of the large-scale flow structures and related heat transport4. Optimization-based Control – Model predictive control techniques applied to machine learning algorithms like reservoir computing to improve learning and prediction
We are looking for highly-motivated and ambitious candidates with excellent knowledge of English (written and spoken) and programming skills. The candidate should have a master/diploma degree or a PhD in mechanical or aerospace engineering, mathematics, physics or computer science (depending on the desired position). Experiences in one or more of the following fields are also desirable: scientific computing/numerics, machine learning, flow measurement techniques, turbulence, dynamical systems and/or control theory, stochastic and/or optimization. The opportunity to obtain a PhD degree is given.
Candidates should send their curriculum vitae, a list of publications (if applicable) and two references.
The university stands in the fields of technology, mathematics and natural sciences, business and media for teaching and research at the highest level. She attaches particular importance to innovative teaching and interdisciplinarity. She identifies with Humboldt's ideals and pursues the vision of a cosmopolitan campus family.
Technische Universität Ilmenau is holder of the „TOTAL E-Quality“ distinction and emphasizes gender equality.
Severely disabled applicants with essentially identical professional suitability will be preferentially selected.
Technische Universität Ilmenau offers flexible working time models.