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”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case
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ethnomycology or ethnobiology large-scale (ethnographic) database construction phylogenetic comparative analyses with Bayesian computational tools The applicant must have the ability to work independently and in
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construction phylogenetic comparative analyses with Bayesian computational tools The applicant must have the ability to work independently and in a structured manner and must be willing and able to cooperate
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complex operational environments. Focus will be given to methods that can derive useful engineering information for the continuously updated digital twins using mechanical response data and environmental
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also be employed for system identification and updating. In addition, the project will benefit from close collaboration with the industry. The outcomes will include advanced condition monitoring system
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to the PhD program within three months from the appointment date. As an employee at NTNU, it is important that you stay updated on professional and organizational changes and adapt to these. The appointment is