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immune-system related diseases such as immunodeficiency and cancer. We use a wide range of techniques such as mouse models, tumor models, in vivo immune cell migration and other functional assays, flow
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responsibilities Design, implement and benchmark deep machine learning models for large-scale cancer datasets that include genomics, transcriptomics, epigenomics and imaging data Collaborate closely with
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research team. The following are considered as advantage: Any previous experience in working with spacecraft data. Deep knowledge of AI methods (esp. machine learning based) and their performance estimation
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project CONFSTAT – Conformally invariant and near critical models in statistical field theory. The work of the postdoctoral researcher will focus on studying conformally invariant models of statistical
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funded by the ERC consolidator grant project CONFSTAT – Conformally invariant and near critical models in statistical field theory. The work of the postdoctoral researcher will focus on studying
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developed will be based on pseudonymization, anonymization, and synthetic data generation. Using real health data as a source of information, we aim to create test datasets and statistical models
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variability and the predictability of mechanistic CH4 models. We aim to fill the knowledge gap in the project “A holistic view of Methane turnover in northern Wetlands by Novel isotopic approach (MeWeN
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models using sophisticate genetic tools, in vivo time-lapse imaging and multi-omics methods to decipher the underpinning mechanisms of regeneration. Our findings provide new targetable mechanisms
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the entire population. The project utilises advanced statistical methods such as multilevel models (mixed models), fixed-effects models, cluster analysis, and sequence analysis. The selected researcher is
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, programming, Linux, data, and infrastructure perspective: short-term projects helping researchers with specific tasks, so that the researchers gain competence to work independently. Provide good role models