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the process of measuring and analyzing high-throughput, multi-dimensional omics data integrating single cell transcriptomics and spatial proteomics of immune cells from CSF and blood as well as human brain
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RNA-seq data for testing and validation. Perform data analysis on large-scale RNA-seq data in pediatric cancer. This may involve the analysis of both scRNA-seq, bulk RNA-seq as well as new single-cell
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Framework (RDF). enables advanced data mining queries using the SPARQL query language. provides a natural language-based interface to perform these queries on the knowledge graph using a large language model
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of very large data sets, data analysis, and simulations of X-ray scattering and spectroscopy signatures of dynamic processes in battery materials. The theoretical/ simulation efforts are supported by
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of science to applied technological research and partnership with industry. Your Tasks, Core Themes Candidates will have the opportunity to pursue one or more of the following core themes: 1. Large Pre-Trained
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from and strongly interact with the Helmholtz large-scale infrastructure project “SAFAtor”. Your responsibilities: Processing DAS data from different ongoing experiments in the study region Application
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, including but not limited to cancer, metabolic disorders, and infections. The research design involves the integration of large-scale biological datasets derived from both the host and the microbiome
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-derived organoid models. You will work closely with in-house technology platforms, including the Single Cell Genomics Facility, Big Data Core and High Throughput Screening Facility. Our research is embedded
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generating, mobilising, and harvesting “big data” to create a dynamic and agnostic collection of information and deliver a new class of research that will enable a better understanding of the clinical
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generating, mobilising, and harvesting “big data” to create a dynamic and agnostic collection of information and deliver a new class of research that will enable a better understanding of the clinical