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Field
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computing, obtained within a maximum of 7 years; application of machine learning and deep learning methods to remote sensing images; proficiency in programming (R, Python, or similar); ability to work
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XRD, and the analysis of complex materials datasets · proficiency in quantitative analysis, computational methods and programming, with experience in AI, machine learning or data-driven materials
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sequencing, imaging and other emerging technologies (e.g. imaging mass cytometry, spatial transcriptomics) – including aspects of informatics and advanced data analysis. You will be responsible
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towards a shared goal. You will be responsible for the design and pilot testing of machine learning-based automated ultrasound video analysis models that incorporate temporal reasoning. The research will
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machine learning approaches to national-scale imaging cohorts including UK Biobank and Our Future Health. Our research is multidisciplinary, with collaborators across leading academic centres in the USA
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University of California, Los Angeles | Los Angeles, California | United States | about 12 hours ago
seeking applicants for a full-time postdoctoral scholar position in Computational Immunology and Genomics. The successful candidate will develop and apply computational, statistical, and machine-learning
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highly motivated and talented Postdoctoral Researcherto work full-time in the field of medical physics and engineering, particularly on low field magnetic resonance imaging (MRI). The position is within
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architecture of entirely new foundation models, directly advancing the frontier of computational biology and machine learning. You will also implement parallel systems capable of training such models across
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Science, MBZUAI. Dr. Ho specializes in distributed machine learning and systems architecture. His expertise drives the design of robust, parallelized systems required to scale massive biomedical imaging models
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developing machine learning pipelines for imaging data. Excellent analytical, problem-solving and communication skills. The ability to work independently and collaboratively within a multidisciplinary research