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to interfere with critical applications that rely on the internet. CRNA was established to address these problems. We combine world-class theoretical and empirical research on the robustness and security of ICT
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comprehensive databases combining nationwide Norwegian health and socioeconomic registry data, biobanks and patient-reported data. Using advanced epidemiological methods, causal inference and machine learning
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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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epidemiological methods, causal inference and machine learning techniques, we aim to: Improve understanding of risk factors for primary headaches Predict diagnosis and disease progression Identify the most
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chemistry, spectroscopy, and computation to iterate design–build–test–learn cycles and to elucidate structure–activity–mechanism relationships. You will join a well-equipped laboratory and a professional
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inference methods, survey design, and/or machine learning Experience with web scraping and API-based data collection Organizational and coordination skills, such as assisting in drafting terms of reference
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(written and spoken) The successful candidate is expected to move to Bergen Desired Qualifications: Experience with Stata Experience with causal inference methods, survey design, and/or machine learning
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the SFF Integreat, The Norwegian Centre for Knowledge-driven Machine Learning (ML) , a centre of excellence funded by RCN and in operation until 2033. The project PI and team are also in close collaboration
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: .return-state Leverandør: .jobbnorge.no Databehandlingsansvarlig: Fingerprint JS Formål: Device fingerprinting for security purposes Personvernregler for databehandling: Fingerprint JS - Personvernregler
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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