73 machine-learning "https:" "Simons Foundation" Fellowship positions at Nature Careers
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image processing, disease detection, diagnosis, and therapeutic monitoring. The program addresses critical regulatory challenges posed by AI devices that can continuously learn and adapt, including
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foundation in computational or statistical genetics. Experience analyzing large-scale datasets is essential, as the work will involve complex genomic and multi-omics data. Familiarity with machine learning
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fellow to join our translational research program in macrophage biology/immunology. Our team takes a systems approach—integrating multi-omics, network science, machine learning, and comprehensive in vitro
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interests in applied statistics, machine learning, or computational biology are encouraged to apply. For more information, please visit our website https://ds.dfci.harvard.edu/postdocs to view the list
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design and discovery, including the use of artificial intelligence (AI) and machine learning (ML) techniques. The hired candidate will focus on computational aspects of immune repertoire analyses
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or translational research experience Knowledge of machine learning, Bayesian modeling, or statistical method development Ideal Personal Attributes: Independent, proactive, and scientifically curious Detail-oriented
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, or comparable research experience, along with significant experience in machine learning, computer programming, computational biological applications. A strong background in statistics and biology. Experience
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spatial transcriptomics to identify potential therapeutics for targeting GBM. For more details, please refer to articles: (Nature communications 2022, https://doi.org/10.1038/s41467-022-33943-0, Cell
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interactions with local centres of excellence in artificial intelligence, machine learning, applied mathematics, and computational sciences Application Procedure We accept applications from students of any
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in Spatial Omics and Multi-Modal Data Integration Duties & Responsibilities: Develop computational and machine learning methods for spatial omics data (spatial transcriptomics, spatial proteomics