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learning methods to understand and investigate the functional behavior of gender-specific cancers. The work will include: Development of ML/DL methods for multi-omics data analysis. Design and implementation
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dynamics of complex molecular systems, and relationships between the presence of biomolecules and synthetic chemical compounds with various disease states. There is also strong research in method and
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on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
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. The project focuses on developing novel representation learning and generative modeling methods to construct a unified cellular morphology state space across heterogeneous datasets. By leveraging shared
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contribute new and better ways to analyse and interpret large-scale data. In your position, you will develop computational methods for cryo-EM reconstruction, heterogeneity analysis, and modeling of structural
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and integrative (proteo-)omics expertise in the lab, guided by leading experts in terminomics, systems-level data analysis, and structural bioinformatics. Your profile A PhD in biology, biochemistry
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. You will collaborate closely with the principal investigator, other postdocs, PhD students, and external collaborators to advance research objectives and generate high-impact results. In
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to the work duties listed above Qualification requirements Appointment to a post-doctoral position requires that the applicant has a PhD, or an international degree deemed equivalent to a PhD, within the
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on bioinformatics analysis of spatial gene expression data as well as other modalities (i.e. microbiome; metabolites, proteins) generated using the Spatial Transcriptomics (ST) method, Spatial metaTranscriptomics
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of human proteins in cell lines using immunofluorescence (ICC-IF) and confocal microscopy. The research group is also applying the latest methods for highly multiplexed immunostaining of cells and tissues