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Field
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Star formation and black hole growth are two of the most important processes in galaxy evolution. Although galaxies and their central black holes operate on very different physical scales
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objective will be to adapt tissue-clearing techniques for multimodal nonlinear imaging while preserving both the structural integrity of the biological specimens and the integrity of the optical signals
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of coronary artery disease and supporting clinical decision-making during catheterization procedures. The project brings together cardiovascular imaging, generative AI, and computational modeling to develop
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Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. | Dortmund, Nordrhein Westfalen | Germany | about 4 hours ago
. or Diploma in bioinformatics or a comparable qualification Extensive programming experience Practical experience in machine learning and the application of large language models Knowledge of OMICS and image
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communication skills in English (verbal and written) Preferred qualifications · Experience with fluorescence or live-cell imaging · Hands-on experience with microfabrication and microfluidics · Familiarity with
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integrating AI with vision at the edge. Despite recent advancements, the synergy between AI and computer vision remains constrained by fundamental imaging bottlenecks. Conventional HDR techniques frequently
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ejection fraction and contribute to the assessment of conditions including heart failure and valvular heart disease. At present, cardiac volumes are predominantly assessed using imaging techniques such as
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computer vision, deep learning, and logical reconstruction techniques. The research investigates how multimodal imaging modalities - including scanning electron microscopy (SEM), photon emission microscopy
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will Gain World-class training in stem-cell differentiation, retinal organoid engineering, vascular biology, and advanced imaging/omics.nei.nih+1 Transnational mobility and secondments across the Pandora
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mass spectrometry data, RNA sequencing, or imaging. The candidate should also have a background in working with hierarchical data and resolving ambiguous, overlapping signals. Strong programming skills