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Field
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: M.Sc. in Computer Science, Machine Learning, or equivalent with interest in Medical Imaging and Deep Learning. Strong knowledge in Machine/Deep Learning with experience in discriminative models
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workflow for the imaging of Mg-LPSO alloys via X-ray nanotomography followed by higher resolution imaging of the identified regions of interest using transmission electron microscopy and atom probe
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(EBSD, EDX, SAXS, TEM) and ex situ/in situ nanoindentation-based techniques (micro-tensile/compression). The insights gained from this investigation will be crucial for optimizing existing Mg-RE alloys
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resonance energy transfer, siRNA mediated gene knock out, cellular imaging, qPCR, etc.) to elucidate unknown interactions between GPCRs and receptor tyrosine kinases at the cellular level. Analyses will focus
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cyanobacteria and Paramecium bursaria. Image data analysis using AI-based tools and programming analysis scripts. Participation in conferences, presentations, and preparation of publications. Intensive exchange
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, understand its role in therapy resistance, and identify novel regulators using innovative CRISPR screening approaches combined with advanced imaging, single-cell transcriptomics, and phospho-proteomics.
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) light source that can suppress spurious nonlinearities in direct drive fusion target compression. As part of the IGNYTe team, you will develop a laser comprised of a shapeable incoherent fibre front
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functional cell assays Interest in translational leukemia research and innovative imaging and omics technologies Bioinformatics experience, especially in R Ideally initial experience with image analysis, laser
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Understanding (Prof. Dr. Martin Weigert) Research areas: Machine Learning, Computer Vision, Image Analysis Tasks: fundamental or applied research in at least one of the following areas: machine learning
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into goal-directed behavior. We use state-of-the-art approaches including functional brain imaging, automated behavioral analysis, and computational neuroanatomy. We value a collaborative atmosphere, early