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Field
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of AI. The ideal candidates will have a background in computer science, statistics, mathematics, or related fields, as well as an interest in social science research methods and theories. The PhD
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are 'explainability' and trust - as the machines learn, they are based upon statistical outcomes on large data sets, rather than human intuitive information. Another problem lies with the fragility of the systems
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candidate, you have an outstanding Master's degree or comparable degree in biology, physics, applied mathematics or related disciplines. As an experimental candidate you have experience in biological systems
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estimation of useful life. In this research, a theory will be postulated for the combined mode of gear failures. The theory will be supported by the basic gear failure mathematics and preliminary validation
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mathematics, (theoretical) computer science, machine learning foundations, electrical engineering, information theory, cryptography, statistics or a related field. - Advanced knowledge of probability theory
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statistical methods are not suitable for big data due to their certain characteristics: heterogeneity, statistical biases, noise accumulations, spurious correlation, and incidental endogeneity. Therefore, big
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Engineering, Biology, Ecology, Forestry or equivalent degree in Life or Mathematical Sciences with background in Ecology. Also, candidates that will obtain their degree in the summer of 2025 are invited