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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 5 hours ago
, and reinforcement learning. Duties and Responsibilities A Postdoctoral Fellow (""postdoc"") is a professional apprenticeship designed to provide recent Ph.D. recipients with an opportunity to develop
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learning. The postdoc will also teach two mathematics courses per year (1 per semester). The appointment is a one-year position with a possibility for an annual renewal of up to 3 years in total
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intelligence (AI) residency) to advance scientific machine learning for clinical oncology. The project builds a hybrid framework that couples an existing quantitative systems pharmacology (QSP) model of cancer
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of at least some of the following: – Extensive independent research experience – Creativity and independence – Experience analyzing hyperspectral data and developing machine learning models - Genetic
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scientific conferences and publish models and scientific insights in high-impact journals Who You Are: Ph.D. in Computational Biology, Bioinformatics, Computer Science or Machine Learning related field
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and machine learning projects involving heterogeneous data. S/he is expected to collaborate and exchange with the previous experts involved in the project and to produce efficient work to achieve
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models of therapeutic response using machine learning combined with Fourier-Transform Infrared Spectroscopy (FTIR) applied to blood, saliva, and tumor tissue samples. Requirements: PhD completed by
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individual to join this exciting collaborative research program that builds upon existing strengths in ADRD biology and genetics (JAX) and bioinformatics methods and machine learning approaches (Roux
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-Coding Genome Edits: Develop innovative machine learning approaches for designing precise non-coding genome edits, focusing on how non-coding alterations influence gene regulation and cellular function
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Koziarski Lab - The Hospital for Sick Children | Central Toronto Roselawn, Ontario | Canada | about 1 month ago
program at The Hospital for Sick Children, University of Toronto, and the Vector Institute. Our research group focuses on developing machine learning-based pipelines that leverage generative models and