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. Required Qualifications: PhD in statistics, economics, computer science, operations research, or related data science fields Strong data science skills, including experience working with large, complex data
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combine large-scale data, computational methods, and clearly articulated social-science theories to improve our understanding of society. Recent advances in machine learning, natural language processing
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for the design of synthetic promoters and data analysis. PhD in (Plant) Biotechnology, Molecular Biology, Biochemistry or equivalent A publication record in peer-reviewed journals, and excellent proficiency in
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experience in Oxford Nanopore Technologies (ONT) sequencing and bioinformatics A track record of research in microbiome science, metagenomics, whole genome sequencing, big data analysis, machine learning, and
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Vitae, 3) PhD degree certification, and 4) contact information of at least four references. The initial appointment is for one year; renewal for additional years is likely pending satisfactory performance
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for large-scale pan-cancer multiomics data. We build on our previous work (e.g., Sanjaya et al. Genome Medicine 2023 ; Pohjonen et al. arXiv 2024 ), developing the new models on the LUMI supercomputer and
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neuro-adaptability with changes in cortical manifestations during an intervention (e.g., non-invasive brain stimulation) for symptom reduction. Large-scale data analysis (e.g. machine-learning) will
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with a multidisciplinary team of clinicians, data scientists, and data engineers to conduct epidemiological research on large-scale electronic datasets and develop common data model specifications
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tools are need during the development of new imaging and sensing systems. With the rapid deployment of data-driven methods, repliable uncertainty quantification remains a big challenge that requires
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genomic analyses on large case-control cohorts to identify risk and response associations for diseases of interest. Performs integrative analyses of other molecular and omics data, such as gene expression