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and fatigue analysis. Experience in coding (e.g., Python) and in the use of structural analysis software (e.g., Abaqus, OpenSees) is desirable. Familiarity with virtual sensing techniques, state
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Python development and contemporary deep learning frameworks such as PyTorch, as well as practical experience with OpenCV or comparable tools for computer vision. You have a good command of classical
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in general. You are very well-versed working with data, models, statistics, simulations, and in general quantitative methods. Basic experience with programming (e.g. python) is a requirement, while
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contributing to open-source Python packages released alongside the methods papers, including close collaboration with the second postdoc on a shared publication pipeline. You will report to the AIMS principal
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environments, such as MATLAB, Python or similar tools, will support your work in the project. Knowledge of Power-to-X, electrolysis, renewable fuels, process integration or energy storage will be advantageous
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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 Power-to-X, electrolysis
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of bioacoustic signals. Extensive experience with programming (Matlab, R, Python) including GPU programming is required, and familiarity with edge-based machine learning (particularly sound event detection), open
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sensing, AI-based methods and data-driven hydrological models, as well as experience in operationalising real-time hydrological systems and programming in, for example, Python. In addition, you are expected
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research questions, and be able to think critically and develop your own scientific ideas. Previous experience with statistical analysis, programming (e.g., R or Python), machine learning, or genomic data
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of satellite-based EO Data Assimilation (DA). Experiences in software development for geospatial applications and proficiency in programming languages (e.g., Python and C++) are highly valued. The teaching