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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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competencies Applicants must hold an MSc degree in statistics, genetic epidemiology, bioinformatics, clinical data science, medicine, or a related field. Programming skills (e.g., R, Python, or similar) and
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representation learning. Programming skills (e.g., Python) and experience with deep learning frameworks (e.g. PyTorch) Interest in applications to ecological or biological networks Good analytical and
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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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methods (e.g., coding in Python/R, working with APIs, scraping data, building or applying models). • Can bridge theoretical insight with concrete technical implementation and empirical analysis. • Is
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analysis. You master programming in for example Python, MATLAB, or a similar platform and you are motivated to further develop your skills in scientific computing and hyperspectral data processing. You have
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systems, large language models, agentic AI, or scientific software development, especially in industry or research settings. Strong software engineering skills, proficiency in Python programming, experience
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of the following: A solid foundation in programming (particularly Python) and modern machine learning frameworks (e.g., PyTorch, TensorFlow). An interest in AI security, trustworthy ML, or the reliability of ML
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programming (particularly Python) and modern machine learning frameworks (e.g., PyTorch, TensorFlow). An interest in AI security, trustworthy ML, or the reliability of ML systems in safety-critical settings