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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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of gene regulatory networks in various cell types. The research will leverage a large in-house dataset from iCAN, complemented by publicly available data. These efforts aim to uncover in more detail
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for an expert in quantitative methods and data analytics. The position involves designing and conducting research utilizing regional big data. The primary task is to develop the field of regional data analytics
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the modeling of both shared and data type-specific variability. By doing so, it provides insights into the regulatory mechanisms underlying cancer. The research will leverage a large in-house dataset from iCAN
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for an expert in quantitative methods and data analytics. The position involves designing and conducting research utilizing regional big data. The primary task is to develop the field of regional data analytics
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metabolism or biomedicine, including the analysis of large-scale structured or unstructured health or biological data, such as electronic health records or biobank datasets, again ideally with publications
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analytics. Virtual screening and efficacy testing using AI-driven approaches to identify promising drug candidates from large chemical libraries. (Retro)synthesis planning using AI to propose feasible
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efficacy testing using AI-driven approaches to identify promising drug candidates from large chemical libraries. (Retro)synthesis planning using AI to propose feasible synthetic routes for candidate
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and curiosity-driven working culture. Research activities are carried out in research groups organised into three multidisciplinary research programmes. For further information please visit https
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and curiosity-driven working culture. Research activities are carried out in research groups organised into three multidisciplinary research programmes. For further information please visit https