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Postdoctoral Research Fellow in Ethics and AI Apply for this job See advertisement About the position Integreat – Norwegian Centre for Knowledge-driven Machine Learning at University of Oslo is looking for a
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-compliance in ecological momentary assessment, or exploring the use of machine learning techniques to aid the estimation of item response theory (IRT) models in small samples. The ideal candidate has prior
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-scale assessment data, meta-analyses of meta-analyses) Methods and approaches to cumulative, living, and community-augmented meta-analyses Methods and approaches to include machine learning and artificial
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with AI algorithms and Machine Learning Fluent oral and written communication skills in English Desired qualifications: Experience with research on epidemiological modelling, with an emphasis on zoonotic
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studies. Proficiency in relevant computational tools and statistical methods. Experience with machine learning in large datasets. Interest and motivation to work in a multidisciplinary team. Ability to work
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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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The idea is to combine established iterative ensemble Kalman methods with novel emerging machine-learning-enabled model calibration techniques recently adopted in CLM-FATES at UiO. The aim is: to constrain
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. Your main tasks will be Develop and apply machine learning techniques and statistical analyses, including novel methodology for analysis of complex polygenic traits and prediction tools for precision
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Develop and apply machine learning techniques and statistical analyses, including digital twin methodology, to fit and validate prediction model. Perform quality control and imputation of genotype and
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understanding of adaptive immune receptor (antibody and T-cell receptor) specificity using high-throughput experimental and computational immunology combined with machine learning. The long-term aim is to