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Field
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of machine learning for engineering applications to start as soon as possible. Be part of change Developing and benchmarking Neural Network architectures (Graph Neural Networks, Transformers, etc
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in their tissue context Development, optimization, and application of bioinformatic pipelines for data integration, analysis, and visualization of complex datasets Computational modeling of gene
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(e.g. RNAi, CRISPR/Cas9, small-molecules). In this context, we also develop new computational tools for automated analysis and data visualization. These include algorithms and software applications
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Universitätsklinikum Heidelberg – UK Mannheim GmbH | Mannheim, Baden W rttemberg | Germany | 5 days ago
optimize bioinformatic tools, databases and workflows Collaborate with researchers to design experiments and analyze results Develop custom scripts and algorithms for data processing and visualization Your
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developing knowledge-graph or information retrieval systems. Funding & Appointment Terms The appointment provides full financial coverage through a dedicated fellowship, comprising: Monthly stipend of €1,650
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | about 6 hours 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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HBIGS Heidelberg Biosciences International Graduate School | Heidelberg, Baden W rttemberg | Germany | 6 days ago
and chromatin and advanced imaging analysis. Marie Curie Doctoral Network ‘AGILE’ Looking to work with and learn from world-leading experts in plant cell imaging? The newly funded Marie Curie Doctoral
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central unit at the Max Planck Institute of Geoanthropology. This facility will support researchers in the collection, integration, and analysis of diverse datasets across disciplines such as geography
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The research group Genomics of Genetic Resources (GGR) research focus is on small grain cereal genome analysis including aspects of structural, comparative, epi- and translational genomics mainly
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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