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(OT) is increasing when it comes to military systems, and the cyber physical aspect is thus of greatest importance for the FACT project. Military platforms are not easily transferable to a relevant
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comprehensive experience with modern machine learning libraries. Published scientific articles in the field of this position. Good written and oral English language skills Preferred selection criteria The optimal
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, including comprehensive experience with modern machine learning libraries. Published scientific articles in the field of this position. Good written and oral English language skills Preferred selection
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Full Announcement and Application at https://www.jobbnorge.no/en/available-jobs/job/260803/phd-fellow-in-knowledge-driven-machine-learning The positionJoin Integreat, a Norwegian centre of excellence
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researchers and industry/user partners Administration and management of infrastructure and networks, including of Linux systems, virtual machines and containers, cloud and edge cloud solutions, and storage
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will join a dynamic team of young, talented, and creative colleagues. The group specialises in applied research within the following areas: Hybrid AI: The combination of machine learning and other system
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to perform structure-based antibody design, developing and employing machine-learning tools for predicting antibody-epitope binding and antibody developability. In silico antibody design is a long-standing
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-throughput experimental and computational immunology combined with machine learning. The long-term aim is to conceive in-silico novel immunodiagnostics and immunotherapeutics using the disease-diagnostic
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structural level, developing and employing machine-learning tools for predicting antibody-epitope binding and antibody developability. In silico antibody design is a long-standing computational and
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techniques to perform structure-based antibody design, developing and employing machine-learning tools for predicting antibody-epitope binding and antibody developability. In silico antibody design is a long