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models for high-dimensional and functional data ”, led by Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available
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, Institute of Basic Medical Sciences (IMB), University of Oslo (UiO), Norway. The candidate shall take part in the research group on “Statistical models for high-dimensional and functional data ”, led by
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and quantitative imaging data Experience in computational modeling of gene regulation and morphogenesis Experience working on high-performance computing environments Personal skills Strong analytical
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regulation and morphogenesis Experience working on high-performance computing environments Personal skills Strong analytical ability and curiosity-driven approach to research Ability to work both independently
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optimization for the compute continuum, across cyber-physical systems (CPS), distributed artificial intelligence (AI), and high-performance computing (HPC), from the data center to the edge devices
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processing research in Norway. Main tasks The main purpose of this post-doctoral position is to qualify for work in high-level scientific positions. A Ph.D. degree is required. Perform research in food
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Learning (ML) , a centre of excellence funded by RCN and in operation until 2033. The research group on statistical models for high-dimensional and functional data is part of the larger and active research
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, including RNA-seq, ChIP-seq, CUT&RUN, and related approaches Performing functional perturbation experiments and integrating multi-omics datasets Collaborating closely with experimental and computational
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sample collection through Biobank1, extracellular vesicles isolation from semen and urine, and downstream multi-omics data analysis. Duties of the position Conduct high-quality research following
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-seq, ChIP-seq, CUT&RUN, and related approaches Performing functional perturbation experiments and integrating multi-omics datasets Collaborating closely with experimental and computational collaborators