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for graph drawing problems Design and analysis of efficient algorithms for hypergraph visualization Computational complexity of graph and hypergraph embedding problems Combinatorial properties and
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of aberrant NR2F1 dosage Design and build an interactive, open-access web platform for visualization and usage of multi-omics datasets Integrate newly generated data with in-house and publicly
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development of ProteomicsDB with a good background in bioinformatics, data visualization, big data analytics at best in the context of mass spectrometry. ProteomicsDB (https://www.proteomicsdb.org/) is an
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for the analysis and integration of single-cell multi-omics data (scRNA-seq, scATAC-seq, Multiome) to reconstruct gene regulatory networks in 3D forebrain dorsal organoids to determine
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Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. | Dortmund, Nordrhein-Westfalen | Germany | 2 days ago
cohorts Evaluation, analysis, and visualization of data Documentation of laboratory work Collaboration with project partners and regular presentation of results Your profile: M.Sc. or Diploma in
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Grafing bei München, Bayern | Germany | about 2 months ago
consortium members and the data analyst team at Helmholtz Munich. The focus of the role will be the analysis of spatial transcriptomics and multi-omics data generated within SPACETIME. Responsibilities include
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Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. | Dortmund, Nordrhein-Westfalen | Germany | 2 months ago
behavior analysis Planning and execution of stimulation and inhibition experiments to dissect signaling pathways regulating neutrophil function in the melanoma context Data processing, analysis
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German Cancer Research Center in the Helmholtz Association (DKFZ) | Heidelberg, Baden-Württemberg | Germany | 2 months ago
Tasks You will take a leading role in the computational analysis of multimodal prostate cancer cohorts, integrating clinical, proteomic, imaging and longitudinal data into clinically meaningful
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acquisition, organization, compression, analysis, and visualization of georeferenced or geometric data on large scales. We put emphasis on methods of distributed computing, machine learning, image and
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, compression, analysis, and visualization of georeferenced or geometric data on large scales. We put emphasis on methods of distributed computing, machine learning, image and text analysis, randomized data