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Field
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to compensate for such aberrations, significantly enhancing image quality. Adaptive requires knowledge of the wavefront to be corrected. Our team has been developing a machine-learning approach to wavefront
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scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available to UK (Home) candidates only. Fully-supervised AI techniques have shown remarkable success in
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learning, or signal processing; familiarity with microscopy data is an asset but not required Interest in foundational machine learning research with applied impact in scientific imaging Demonstrated
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Funding for: UK/Home Students We invite applications for a fully funded PhD research scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available
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that exhibit emergent turbulent behaviors, and (2) disordered optical media that process information through complex light scattering patterns. Using advanced imaging, machine learning techniques, and real-time
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Systems, or a related field. Strong analytical and critical thinking skills. Strong machine learning (ML), computer vision (CV), large language models (LLM) for quantitative data, texts, images, and sensor
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- and time-specific innervation that extends into adolescence. Our lab has used whole-brain tissue clearing, light-sheet imaging, and machine learning to map the spatial and temporal dynamics of serotonin
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domains are e.g., signal-/image processing, artificial intelligence and machine learning. Tasks: research and development in designing and programming field programmable gate arrays (FPGAs) for accelerating
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and curate LC-MS/MS data for high-quality feature extraction Design and train machine-learning models for mass spectrometry and chemometric data Integrate multi-omic data including genomics and
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of machine learning frameworks Your research will include using models and codes to investigate the optimized design, integration, and intelligent operation of thermal energy storage systems in industrial