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the individual's research skills for his/her primary benefit. This multidisciplinary program is focused on developing the next generation of researchers in the field of aging, and competitive candidates will
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, and behavioral measures. · Collect, process, and analyze multimodal datasets, including neural, physiological, and motion-tracking signals. · Develop and refine research protocols and methodologies in
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Systems Modeling. This position focuses on leveraging and developing new equation learning methods, such as Physics-Informed Neural Networks (PINNs), Biologically Informed Neural Networks (BINNs), and
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develop novel computational approaches. Develop mathematical descriptions for the acquired data and work with our theorists collaborators to implement new theories. Integrate with the rest of the lab and
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the Interpretable Machine Learning Lab (https://users.cs.duke.edu/~cynthia/home.html ) for a scientific developer to work in collaboration with other researchers on machine learning tools that help humans make better
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-funded R01 project to develop and disseminate computational neuron models and their integration with TES and TMS electric field simulations in the popular SimNIBS software package. This project is a
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will develop novel statistical and machine learning methods for any of the following: multi-omics data (such as bulk and large-scale single-cell RNA sequencing data, spatial transcriptomics, bulk and
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for projects examining questions in cardiovascular disease using extremely large data sets comprised of routinely-collected clinical data · Developing and maintaining requirements for access to Truveta Data and
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research or scholarship. The appointment is generally preparatory for a full time academic or research career. The appointment is not part of a clinical training program, unless research training under
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Associate. Occupational Summary Responsibilities will include developing next-generation genome engineering technologies for gene and cell therapy applications as well as functional genomics. In particular