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learning for material characterisation and property prediction, finite element modelling, structural optimisation, and experimental testing of timber, bonded interfaces and laminated components. The
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foundations in machine learning, linear algebra, probability and optimisation; and strong Python and PyTorch (or comparable framework) skills. Only applicants who demonstrate substantial hands-on experience
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, data analysis and programming skills (e.g., R, python, matlab, etc). We seek candidates that are team players who can play active scientific and social roles in a diverse and international lab setting
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acquisition, equipment construction, machine learning, and image analysis. You master programming in for example Python, MATLAB, or a similar platform and you are motivated to further develop your
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. Scientific computing and reproducible research workflows. Statistics, machine learning, artificial intelligence, optimisation, uncertainty or scenario analysis. International trade, production facility
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programming skills in Python, MATLAB/Simulink, or a comparable scientific computing environment. A fundamental understanding of energy-conversion systems, thermodynamics, electrical engineering, heat and mass
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optimisation for well-controlled materials and processes, you will investigate whether similar methods can be applied to mechanical recycling from heterogeneous waste streams. The ambition is to show how
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particular software package is not required, but experience with one or more computational environments, such as MATLAB, Python or similar tools, will support your work in the project. Knowledge of