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The Rantalainen group is focused on application of machine learning and AI for development and validation of predictive models for cancer precision medicine, with a particular focus computational pathology. Our
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gene regulation (Aguilo Lab). The other group, based in the Department of Physics, develops mathematical models to understand the dynamics of a wide range of living systems, such as gene regulation
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clinical applications of novel cell and gene therapies in one integrated setting. In this environment, our research group focuses on combining novel genome engineering tools (e.g., CRISPR-based) and
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computationally challenging. To address this, our research employs advanced computational methods to simplify high-fidelity 1-D hydrodynamic models based on Partial Differential Equations (PDEs). This approach
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spinal cord as a model system. You will engage a systematic strategy to identify these mechanisms by generating innovative mouse genetic strains, identifying embryonic defects and the underlying molecular
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and in vivo model systems, applying multiple omics methods. You will be working with clinical samples, method development and several molecular biology techniques, especially PCR and sequencing as
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the following responsibilities: Designing and performing experiments to study skin regeneration, inflammation, and fibrosis using human tissue samples and preclinical models. Applying advanced molecular and
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will join a multidisciplinary research program that combines experimental models, patient-derived materials, and advanced technologies to explore the mechanisms that preserve auditory system homeostasis
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, their interactions with hosts and the environment, and how they are transmitted through populations. Research will have a strong focus on computational analysis or predictive modeling of pathogen biology or host
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evolution, and further to modelling. Investigating transcriptional profile of lncRNA. It can be expression quantification, alternative splicing, start sites properties. Performing AI modeling and developing a