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mechanisms such as fatigue, fracture, ultimate strength, biofouling, corrosion, etc. Assess the metocean statistics to obtain 50- and 100-year return period load combinations. Develop and document high
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, statistics, physics or relevant fields. Strong background in machine learning, preferably experience in probabilistic modeling, Bayesian machine learning, or graph representation learning. Programming skills
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degree in computer science, mathematics, statistics, physics or relevant fields. Strong background in machine learning, preferably experience in probabilistic modeling, Bayesian machine learning, or graph
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statistical models to understand the ecological and phylogenetic factors playing a role in the evolution of the host-microbiome systems. The project will use the vast number of paired host and microbiome
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analysis in software such as R, Stata, Python or similar tools. You are comfortable working with datasets, developing statistical models and documenting your analytical choices. Experience with experimental
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primarily be using registry data from Statistics Denmark. Over the three years of employment, you will be required to deliver 600 hours of teaching and attend 30 ECTS of PhD level training. Your competencies