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Field
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scientific knowledge, such as physical laws, differential equations, and domain-specific constraints, to model, simulate, and understand complex systems. The project will explore modern SciML methods
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resources efficiently. In this PhD project, you will develop mathematical theory and computational methods for the analysis and design of chaotic sampling mechanisms in networked control systems. You will
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-of-the-art in failure propagation analysis and safety assessment of complex systems. In particular, it will investigate extensions of existing formalisms to deal with aspects such as the timing of fault
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Engineering, Bioinformatics, Statistics, Applied Mathematics, Physics, or a closely related STEM field. Demonstrated experience developing AI and machine learning models for biomedical applications. Job
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, Mathematics, Engineering or a related discipline, with an interest in AI applied to complex and safety-critical systems. Good knowledge of Machine Learning and Deep Learning methods, including experience with
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: Completed, or soon-to-be completed MSc in the biological sciences or different fields in the natural sciences (e.g. computational, mathematical, earth or marine sciences) with a strong interest in ecology and
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Infrastructure? No Offer Description The AITAX project aims to address the complexity and dynamics of modern tax regulations by developing a cutting-edge AI solution for automating the analysis and processing
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Infrastructure? No Offer Description The AITAX project aims to address the complexity and dynamics of modern tax regulations by developing a cutting-edge AI solution for automating the analysis and processing
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Infrastructure? No Offer Description The AITAX project aims to address the complexity and dynamics of modern tax regulations by developing a cutting-edge AI solution for automating the analysis and processing
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computer science, image analysis and machine learning, engineering physics, data science, applied mathematics, molecular biotechnology engineering, or another related field; or Have completed at least 240