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focuses on understanding the molecular and cellular mechanisms of systemic amyloid deposition, with particular emphasis on AL and ATTR amyloidosis. We use in situ structural approaches to visualize amyloid
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through multi-modal sensing, autonomous UAV platforms, and advanced AI-based analysis. A central focus is the detection of defects in bridges and viaducts using X-ray, LiDAR, visual and acoustic data
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that enable decision-making across Northeastern University. This role bridges data engineering and analytics—building robust data pipelines in Snowflake while creating user-friendly reporting and visualization
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Science and the Cognition and Perception Program of the Department of Psychology at New York University have a one-year postdoc position available, supported by our Visual Neuroscience Training Program. It
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://www.tuni.fi/en/research/financial-data-science https://www.tuni.fi/en/research/computational-intelligence-group-coin Job description and requirements We are looking for two highly motivated bachelor/master
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, science and technology, and professional disciplines at the undergraduate, master’s and doctoral levels. JMU has achieved national recognition for the high quality of its academic programs, focus
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visualization of high-dimensional omics data from the field of bioeconomics, and in the modeling, simulation, and engineering of biomolecular systems, including enzymes. Apply your data science skills to real
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for sustainable landscape planning and their ecological, visual and social aspects. This aim aligns with Aalto’s mission to shape sustainable futures: these environments form the spatial foundation on which
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Implementation Science Interventions Employee Category: Research Staff Location: Kent Ridge Campus Posted On: 20/02/2026 ▲ Collapse Job Title: Senior/Research Fellow - BISI University-Level Unit: Yong Loo Lin
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on scientific applications of AI (see https://www.anthropic.com/news/anthropic-partners-with-allen-institute-and-howard-hughes-medical-institute ). What you’ll do: Design and implement scalable, robust data