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Field
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education corresponding to a five-year master’s degree with a learning outcome corresponding to the descriptions in the Norwegian Qualification Framework, second cycle. The applicant must have a documented
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to WAN and inter-domain networking, Excellent command of foundational and applied AI technology, from neural networks, distributed reinforcement learning to agentic AI and recent developments in
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for positions such as postdoctoral fellow, research fellow, scientific assistant and specialist candidate Preferred selection criteria previous experience from industry or research in engineer-to-order
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large datasets, and applying AI approaches (e.g. machine learning, image segmentation, multimodal AI data integration) will be considered advantageous. Strong skills in communicating scientific results
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patients Experience with clinical data collection Familiarity with epidemiological methods and registry-based research, epigenetic analyses or machine learning. Interest or experience in science
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registry-based research, epigenetic analyses or machine learning. Interest or experience in science communication and public engagement Experience with publishing biomedical papers Experience with open
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need, this may be prepared from published field operation data, laboratory measurement or other sources. Machine learning can be used to select the bet data set for each particular cases covering
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% statutory contribution to the State Pension Fund is deducted from the salary. The employment period is 3 years without teaching duties. If learning Norwegian (level A2 corresponding to at least 15 credits) is
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willingness to learn bioinformatics are required. Skills where hands-on experience is considered an advantage: functional genomics (e.g. ChIP-seq, RNA-seq, ATAC-seq) genotype-phenotype associations (e.g. GWAS
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is important that you are able to: Willingness to learn new fields in a multidisciplinary research environment Problem solver, with ability to use existing knowledge in new ways Good communication