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-principles physical knowledge with data-driven learning to enable continuous, autonomous system oversight. Research objectives The project pursues two interconnected research directions: Continuous
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dynamic, crowded environments. As a PhD candidate, you will develop methods that combine data-driven autonomy with formal safety guarantees and validate them in real time through simulation and experimental
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scenarios, and analyse the resulting data. You might further engage in qualitative follow-up studies gaining in-depth knowledge about the underlying mechanisms. A central part of your work will be
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The Danish Confederation of Professional Associations on Academics in the State. Questions For specific information about the PhD fellowship, please contact the principal supervisor. General information
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machine learning models that can detect unusual, unsafe, or attacked operating conditions. Developing data-driven models that capture how faults and attacks spread through a system, and using them to make
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in one of the general degree programmes at DTU. For information about our enrolment requirements and the general planning of the PhD study programme, please see DTU's rules for the PhD education
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training course within the first year of employment. The candidate will most likely be expected to conduct teaching, supervision, and examination within the Danish programme. Applicants can find information
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training course within the first year of employment. The candidate will most likely be expected to conduct teaching, supervision, and examination within the English programme. Applicants can find information
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will be part of a team of researchers responsible for the annual productivity studies by Aalborg University Business School. These studies provide data driven insights into regional productivity
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will find contact persons at the bottom of the jobpost. Further information Read more about our recruitment process here. The assessment of candidates for the position will be carried out by qualified