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have the experience below, please do highlight where transferable skills would assist with you undertaking the role. Qualifications PhD, or equivalent professional experience, in Machine Learning
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, machine learning and surrogate modeling; and presentation skills and proven research independence. Application material should include a cover letter, CV, and the names and contact information of three
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interest in social science applications, and with strong competence in statistics and machine learning. The successful candidate will develop predictive models using machine learning and work alongside other
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supporting better patient outcomes. The successful candidate will lead the development of multi-modal MRI foundation models that integrate imaging data and radiology reports. Using advanced deep learning
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, quantitative polymerase chain reactions, next-generation sequencing, and related, advanced molecular biology techniques. The person will instruct staff and students, participate in planning experiments, and
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 18 hours ago
to): Develop machine learning algorithms that utilize fire products from geostationary satellites to better represent fire evolution and variability Develop machine learning emulators to represent forward
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, ATAC-seq, CUT&RUN, MERFISH, Visium), epigenomic data processing (chromatin accessibility, histone marks, enhancer mapping), multi-omics integration using Seurat, Signac, Harmony, ArchR or Scanpy, machine
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and aggression, using optogenetics, in vivo imaging, electrophysiology, and sophisticated machine learning/artificial intelligence analyses of mouse behavior. All projects have translational components
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/chercheur-fh-en-simulation-des-deformations-des-tis… Requirements Research FieldEngineering » Computer engineeringEducation LevelPhD or equivalent Research FieldBiological sciences » Biological
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developing new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health