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7th February 2025 Languages English Norsk Bokmål English English PhD Fellowship in Statistical Physics and Machine learning modelling for battery materials Apply for this job See advertisement Job
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interest and competence in practical application of statistical learning or machine learning Working environment: The project will be done in an interdisciplinary team. The successful candidate will work in
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/or FE simulation results and modern machine learning methods may be evaluated for this purpose. The current project is relevant for a wide range of academic and engineering disciplines, including
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create statistically significant data. Use the measurement data to improve digital models of the structure and the load process for relevant excitation, such as traffic and wind. Explore machine learning
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and aerodynamic loads. The project will be co-supervised by experts in aeroelasticity and machine learning and can include aspects of fluid-structure interaction and digital twins depending
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is essential for the successful completion of this PhD project. Key Qualifications: A solid foundation in energy technology and a strong understanding of artificial intelligence (AI), machine learning
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methods. Knowledge of machine learning models applied to material modeling. Proficiency in programming languages, such as Python, MATLAB, Julia, and C++, or molecular dynamics programs, such as LAMMPS
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topics: Microscopy Bioimaging Cell biology Machine learning Optics related courses (such as Fourier, Statistical) Computational imaging/modelling Digital image processing/analysis Experience in multi
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evaluation in collaboration with the centre’s user partners such as KSAT. We are therefore seeking a computer scientist or similar with a strong interest and competence in deep learning. Working environment