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of AMR enzymes in this process. The research will be tailored to the candidate’s expertise and interests, with emphasis on interdisciplinary collaboration and openness to learning new methodologies
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enzymes in this process. The research will be tailored to the candidate’s expertise and interests, with emphasis on interdisciplinary collaboration and openness to learning new methodologies
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well as data analysis of large-scale registry data comparing mutation carriers to non-carriers. The aim of the project is to generate novel insights into human endogenous opioid function. The project offers
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interest in learning the relevant topics of the PhD project and associated areas such as experimental design, laboratory methods, as well as statistical analysis. The candidate must have the ability to work
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invites applicants for four PhD Fellowships in subsurface characterization within geosciences, reservoir engineering, molecular modelling, and machine learning at the Faculty of Science and Technology
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performed in close collaboration with experienced team members. Additionally, the candidate will acquire skills in performing in vivo PET/SPECT and MR/CT imaging experiments and data analysis. The candidate
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for Knowledge-driven Machine Learning. We are looking for a motivated researcher, who has experience with both theoretical, methodological and applied research in change and anomaly detection in sequential data
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, and AI chatbot chats; (b) quantitative content analysis; (c)text mining and machine learning methods; (d) survey design and public opinion research; (e) election studies; (f) the Norwegian political
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properties of the Higgs boson. The group focuses on final states containing several tau-leptons. The analysis activity is now extended to include generic anomaly searches using Machine Learning. Furthermore
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anomaly searches using Machine Learning. Furthermore, the group takes part in ATLAS upgrade, with participation in the ITk-Pixels project, with responsibilities concerning testing and delivery of pixel