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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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”, 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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interdisciplinary center with joint efforts in theory, computer simulations and experiments, both in fundamental and in more applied directions. The center works to advance the understanding of porous media by
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the PhD candidate may include (non-)linear inverse load estimation and data-driven/machine learning techniques that rely on physics-informed guidance for improved robustness. A key task will be to quantify
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to a five-year Norwegian degree program, where 120 credits are obtained at master's level Preferred qualifications: Experience with computer-based analytical tools Experience with experimental evolution
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with contactless sensor which can be integrated into existing data acquisition system enabling to monitor propulsion health as well as estimating remaining useful life (RUL). This PhD project is
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Fulfil administrative and teaching duties required (if applicable) Be prepared for changes to your work duties after employment. Required selection criteria You must have a relevant background in Computer