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Field
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helps build the brain? Ribosomes are complex molecular structures that play active roles in protein synthesis. During development, progenitor cells generate a diverse pool of neurons that form the adult
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developed mouse models and human tissue processing pipelines. The candidate will learn and use cutting edge immunological methods (high parameter flow cytometry, multiplex imaging, single cell and spatial
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biology, molecular biology and animal models to investigate how these hybrid cells favour carcinogenesis. Where to apply Website https://app.smartsheet.au/b/form/019eedeef91978fdbc2b67718479eac6?project_id
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AITHYRA GmbH - Research Institute for Biomedical Artificial Intelligence of the Austrian Academy of Sciences | Vienna, Virginia | United States | about 9 hours ago
(e.g. geometric deep learning, generative models, representation learning) to decode molecular physiology, pathology, and therapeutic design? Are you passionate about high-throughput biology and scalable
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liquid biomarkers for leukodystrophies by integrating multi-omics data from patient plasma and iPSC-derived human disease models. You will investigate disease-related molecular signatures using
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to medium level skills in programming, image analysis and electronics • Solid knowledge and hands-on experience in light and electron microscopy. • Solid formation in genetics, statistics and molecular
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Monash/Bayreuth Double PhD Degree Opportunity - Accelerating organic semiconductor discovery with generative AI, molecular dynamics and DFT Job No.: 698040 Location: Clayton (home) and Bayreuth
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STLO) (https://eng-stlo.rennes.hub.inrae.fr/ ). This work is part of the “Predicting the browning of dairy powders by kinetic modeling of Maillard reaction and caramelization during drying and storage
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glycans. You will combine glycan-based tools, molecular biology, and biochemical or analytical approaches to identify and characterise glycan receptors that drive influenza host and tissue tropism. We
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plasma and iPSC-derived human disease models. You will investigate disease-related molecular signatures using metabolomics, lipidomics, proteomics and genomics, and combine these data using statistical and