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Biophysical and Biomedical Measurement Group, National Institute of Standards and Technology | Gaithersburg, Maryland | United States | 2 months ago
the Biophysical Metrology and Inference program at NIST, which develops interpretable measurement approaches, physical models, and data-analysis methods to connect biological signals to quantitative mechanism and
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of existing AI/ML tools to assign structures to unidentified peaks in metabolomic datasets (e.g., https://www.nature.com/articles/s42256-021-00407-x ), and extending these tools or developing new models as
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Tranplantation and Regenerative Medicine Postdoc Appointment Term: 1-year term (Renewal is based on performance -- Max of a 5 years) Appointment Start Date: As Soon As Possible Group or Departmental Website: https
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codes and process their results. Helping to develop new models and algorithms to simulate pulse propagation, the material response, and other aspects of our experiments. Coding in Julia and python. Take a
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for creating an energy systems modeling tool that models energy resources, technologies, and loads for specific applications. The postdoctoral associate will conduct research to assess the impacts of different
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of Electrical Engineering, Division of Electric Power Engineering. The position will include characterising commercial cells and the production maturity through performance testing, teardown analysis and material
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Columbus, Ohio. More information about the lab is available at https://sun-hongyu.github.io/ . We welcome applicants from diverse disciplinary backgrounds, including geophysics, seismology, data science
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both 2D and 3D models, for experimental studies. The ideal candidate will demonstrate a strong record of accomplishment in a relevant area of biomedical research, evidenced by first-author peer-reviewed
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experimental approaches such as non-coding CRISPR screens, the Massively Parallel Reporter Assay (MPRA), saturation mutagenesis, and synthetic sequence design, alongside machine-learning models of regulatory
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different types of epidemic models, including spatial and individual-based models. Methodologically, the work will focus on multi-objective, hierarchical, and explainable reinforcement learning, as