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cytoskeleton and membrane trafficking. • Utilize quantitative, biophysical, and computational approaches to cell biology, including fluorescence imaging, image analysis, and biophysical modeling. • Experiment
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Apply now to Postdoctoral Scholar in Imaging Science/Cystic Fibrosis/Rhinology Job #JPF10530 Surgery-Head & Neck / David Geffen School Of Medicin / UCLA Position overview Position title: Postdoctoral
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, computational neuroscience, or a related field. Experience with brain stimulation, neurophysiology, or neural signal processing. Familiarity with tools such as Python, R, or similar for data analysis. Interest in
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, statistics, and/or computer science. These skills would be applied to large mass spectrometry and mass spectrometry imaging datasets for clinical research, focusing on malignancies including breast and lung
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Department of Systems Biology invites applicants for a senior level research position of Associate Research Scientist. Candidates should have strong scientific and postdoctoral experience. The integration
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disease with a focus on Alzheimer’s Disease. Computational models will be developed that utilize data obtained from a wide range of experiments, from basic biochemical methods to advanced imaging techniques
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Biology, Bioinformatics, Cancer Biology, Molecular Biology, Pathology, or a closely related discipline. Strong background in spatial omics technologies (e.g., spatial transcriptomics, high-plex imaging
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from the Department of Civil and Environmental Engineering. This position is part of an exciting research programme aimed at advancing the multi-robotic wire-arc directed energy deposition technology and
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), located in central Berlin, is seeking a highly motivated postdoctoral researcher with a strong computational background to develop new methods for analyzing multimodal data from genetic and pharmacological
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analysis, and may contribute to scientific publications. The CRF will work closely with the broader multidisciplinary team, including postdoctoral researchers in biomedical engineering and computer vision