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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 develop and evaluate artificial intelligence methods for physical medical procedures. The fellow will design and implement machine learning models to analyze procedural data, support clinical
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of machine learning researchers, engineers, and domain scientists. This collaborative environment offers a unique opportunity to work on cutting edge AI models, with a focus on generalization and
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of the distinguished Association of American Universities. Connections working at New York University More Jobs from This Employer https://main.hercjobs.org/jobs/22209243/postdoctoral-associate-x28-chen-lab-x29 Return
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 10 days ago
methodology, machine learning, and biomedical data science. Our research develops rigorous and interpretable methods for high-dimensional biomedical data, with applications spanning cancer genomics
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School graduates over a thousand students who are ready to take on great ambitions and challenges. For more details, please view: https://www.ntu.edu.sg/eee We are looking for a Postdoctoral Fellow to work
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, Applied Mathematics, or Computational Physics/Chemistry with a strong ML focus. Technical: Deep understanding of Deep Learning (Transformers, GNNs, Auto-encoders). Programming: Proficiency in Python and
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science, or a related field; experience with using and building machine learning models, developing and validating computational analysis workflows, and developing circuit models is preferred; excellent
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genes, neural circuits, and behavior. For more information about our work, visit https://www.bayer-lab.org/research This position is for a post-PhD trainee. The Postdoctoral Associate will develop and
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well as interpretability of scientific foundation models. The rush to build foundation models has led to the development of large machine learning models in Astrophysics, fluid dynamics, biology, weather prediction, solar