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. Applicant(s) should possess a Ph.D. degree, or equivalent, in biological or computer sciences or a related discipline. Experience in any of the following fields, including 1) biological image acquisition
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studies (e.g., EELS, EDS) to probe defect structures and dynamics Apply advanced image processing and analysis; develop AI/ML workflows for quantitative defect characterization Implement high-throughput and
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images, and GPS information. Postdoctoral researchers work on independent research under a faculty member’s guidance, teach 2 quarter-length courses per year, and participate in the academic life
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machine learning methods. The successful candidate will lead an independent research project dedicated to identifying abnormal behavior and neuronal activities in circuits of murine models of 22q11.2 and
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
researcher with a background in machine learning and/or biomedical image processing. The ACM Lab is developing software to support AI-driven techniques to rapidly diagnose, track, and treat neurodisorders
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. Proficiency in Python and/or machine learning applications for data analysis. Ability to work independently and manage multiple research activities. Experience contributing to academic publications and
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handling Experience with data analysis and neuronal recordings Experience with visual discrimination experiments on mice Experience with machine learning techniques and neural network modeling Evaluation
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a novel multi-omics approach that integrates high-throughput imaging and machine learning methods with CRISPR/Cas9 screens and saturation mutagenesis to answer central questions about the
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of massive galaxies from the primordial Universe to z~2. This project combines a unique JWST dataset with state-of-the art hydrodynamical simulations and machine learning techniques to understand the origins
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Radboud University Medical Center (Radboudumc); 10 Oct ’25 published | Netherlands | about 1 month ago
Image Analysis Group (DIAG) at Radboudumc. We develop, validate and deploy novel medical image analysis methods, usually based on the newest advances in machine learning with a focus on computer-aided