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applications. Key Responsibilities: Develop and fine-tune computer-vision models, instance segmentation, and retrieval-based estimation from images and text metadata. Build and evaluate monocular depth pipelines
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on advanced modeling of coupled fracture/damage of geomechanical materials when subjected a range of multiphysical loading regimes including hydraulic, thermal, and chemical processes. The direct applications
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brain health. This project combines MRI and EEG/MEG studies with behavioral, genetic, and clinical measures to yield a more complete picture of how processing speed is related to health outcomes
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correlations, probabilistic evaluation, scalability). Prototype and evaluate data system components or extensions to existing open-source engines. Publish in top-tier conferences (SIGMOD, VLDB, PODS, ICDE, ICDT
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to recruit a research assistant to develop AI-enabled healthcare applications. Key Responsibilities: Develop and fine-tune computer-vision models, instance segmentation, and retrieval-based estimation from
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more complete picture of how processing speed is related to health outcomes. The candidate will work in a multidisciplinary environment consisting of PhD-level scientists, graduate students and
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computational biophysics Machine learning and data analysis for biological systems Biomedical imaging and signal processing Molecular modeling and simulations AI applications in bioinformatics or health sciences
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models related to modern data management challenges (e.g., query feasibility, data correlations, probabilistic evaluation, scalability). Prototype and evaluate data system components or extensions
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brain health. This project combines MRI and EEG/MEG studies with behavioral, genetic, and clinical measures to yield a more complete picture of how processing speed is related to health outcomes
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AI systems capable of generating data such as text, images, sounds, graphs and other data types. Students will explore the core principles behind generative models, including advanced transformer