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biomarkers and patient cohorts Evaluation, analysis, and visualization of data Documentation of laboratory work Collaboration with project partners and regular presentation of results Your profile: M.Sc
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | 1 day ago
on gas bubble generation on micro electrodes # Visualize and analyze bubble dynamics using advanced optical measurement techniques # Measure the flow velocity and temperature distribution
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microscopy) MALDI MSI, laser microdissection MS, and LC-MS analyses to identify metabolite, lipid, and protein profiles in murine tissue and blood Evaluation, analysis, and visualization of data Documentation
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-oncological risk assessment Evaluation, analysis, and visualization of data Documentation of laboratory work Collaboration with project partners and regular presentation of results Your profile: M.Sc. or a
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samples Evaluation, analysis, and visualization of data Documentation of laboratory work Collaboration with project partners and regular presentation of results Your profile: M.Sc. or a degree in a natural
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, or willingness to acquire these skills) and have experience in archival research and/or (2) experience in computational methods of text mining, analysis, and visualization (e.g. Python) or analysis of biodiversity
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formats, ensuring data quality and plausibility, linking entities across sources, integrating spatial and demographic information, and providing structured access for analysis and visualization. This thesis
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work at the interface between computational spatial omics, single-cell mass spectrometry (MS)-based proteomics, multiplex imaging, and bioinformatics. We have recently co-developed Deep Visual Proteomics
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, the corresponding methods should be extended, if necessary, to be understandable for non-experts as well. This can be achieved, for example, thru visualizations or the automated extraction of the most important input
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visual age estimation Object detection (e.g., using YOLO, RT-DETRv2, among others) Image classification based on common architectures (e.g., Transformers: ViT, Swin, DeiT; CNNs: ResNet, EfficientNet; GNNs