PhD Student (m/f/d) Research focus: Integration of machine learning methods into numerical simulation
Task description
We are looking for a motivated and innovative PhD student to investigate integration of machine learning methods into numerical simulation and to build up in-depth knowledge of hybrid model approaches which combines physics-based models with data-driven methods.
The work within the scope of this position includes on the one hand fundamental research on novel combinations of numerical methods and machine learning approaches, and on the other hand applied research in the field of machine learning integration into computational fluid dynamics. Comprehensive knowledge in this area is already available at LEC so the candidate can build on existing models. Nevertheless, the creativity of generating smart methods and contributing novel ideas is obligatory.
The scientific outcome of this work should be an improved understanding of hybrid modeling within the framework of energy systems. Furthermore, a profound knowledge about the potential and limitations of the application of data-driven methods in computational fluid dynamics should be elaborated.
PhD Student (m/f/d) Research focus: Integration of machine learning methods into numerical simulation
We are looking for a motivated and innovative PhD student to investigate integration of machine learning methods into numerical simulation and to build up in-depth knowledge of hybrid model approaches which combines physics-based models with data-driven methods.
The work within the scope of this position includes on the one hand fundamental research on novel combinations of numerical methods and machine learning approaches, and on the other hand applied research in the field of machine learning integration into computational fluid dynamics. Comprehensive knowledge in this area is already available at LEC so the candidate can build on existing models. Nevertheless, the creativity of generating smart methods and contributing novel ideas is obligatory.
The scientific outcome of this work should be an improved understanding of hybrid modeling within the framework of energy systems. Furthermore, a profound knowledge about the potential and limitations of the application of data-driven methods in computational fluid dynamics should be elaborated.
PhD Student (m/f/d) Research focus: Integration of machine learning methods into numerical simulation
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The LEC supports equal opportunities and diversity. We are looking for dedicated and motivated individuals with research talent.
Kontakt:
Herlinde Kohlmaier
Email: career@lec.tugraz.at