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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 16 hours ago
study investigating the effects of stress on the brain and the immune system. This study will utilize functional magnetic resonance imaging (fMRI) to index brain activity during stress and will use a
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focus will be on biomechanics, image processing, machine learning (ML), artificial intelligence (AI), and metrology, the student will also contribute to the co-design of cadaver experiments and data
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stem cells as well as metabolomic analysis. You have documented experience in performing high-content image-based screening. You have an active interest in stem cell and developmental biology Proficient
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to understand, predict, and treat diseases. You will work with multimodal biomedical datasets including omics, imaging, and patient data and apply cutting-edge AI models such as graph neural networks, transformer
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to research this topic. Interest in laboratory work and basic technical understanding. Fluent written and spoken English. Programming skills in e.g. Python, R, Matlab and Java. Experience in image processing
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multimodal brain imaging techniques with novel neuromodulation. More specifically, we work to understand the mechanisms of (mal)adaptive plasticity and develop new treatment approaches for different
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motivation to learn, cryo-EM data collection, image processing, and 3D structural analysis. Prior experience with cryo-EM single-particle analysis (SPA) and/or cryo-electron tomography (cryo-ET) is a strong
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image sequences. As a benchmark, end-to-end deep learning models will be developed using raw image data. In parallel, shallow learning models (e.g., Gaussian processes) will be explored based on insights
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of cardiac arrhythmias and will be facilitating the experimental procedures involving cardiovascular electrophysiology and high-resolution magnetic resonance imaging (MRI). This research group host ~20
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! Scientists have speculated about the inherent potential of molecular systems for information processing since the dawn of computer science. However, the digital computing paradigm is inherently ill-suited