11 machine-learning "https:" "https:" "https:" "https:" Fellowship positions at Nature Careers in United States
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of immune cell function. These projects are focused on making safer and more effective cell therapies (e.g., CAR-T) and gene therapies for cancer and beyond. We are an interdisciplinary lab spanning
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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
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. graduates and doctoral candidates nearing graduation who have research interests in applied statistics, machine learning, or computational biology to apply for our postdoctoral fellows program. This program
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tissue specimens Assemble analysis pipelines using machine learning to process tissue data reproducibly and at scale Conduct analyses using programming languages such as R and Python Collaborate with other
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uses long timescale molecular dynamics (MD) simulations, integrated with experimental observables (especially cryo-electron microscopy data), and machine learning tools to better capture the dynamics
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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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mathematics, biophysics, AI/machine learning, computational biology, computer science/engineering, statistical inference, or related fields are particularly encouraged to apply. POSITION DESCRIPTION Flatiron
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large scale flow network simulations, machine learning, and methods from topological data analysis, to a broad set of problems. Examples include modeling vertebrate and invertebrate circulatory systems
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image analysis Ability to do original and outstanding research in computational biology, and expertise in computational methods, data analysis, software and algorithm development, modeling machine
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, and expertise in computational methods, data analysis, software and algorithm development, modeling machine learning, and scientific simulation Ability to work well in an interdisciplinary environment