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Field
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for high-dimensional dependent data, and data sketching approaches for massive data. Opportunities to Contribute: Develop statistical/machine learning methodology for multi-modal imaging data integration
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to join an interdisciplinary project at the interface of single-cell genomics, data integration, and developmental biology. The project focuses on the integration and analysis of multi-modal single-cell
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imaging modality. Preferred Qualifications: Work Experience: Preclinical MRI MRI pulse sequence development and optimization Quantitative MRI analysis Image reconstruction and image processing using Bruker
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The successful candidate will have extensive experience with bioinformatics (the analysis of bulk and single-cell high-throughput datasets) and will contribute to a research program investigating
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patterns, identify changes in behavior and potentially unsafe events, and support safer and more independent living. Approaches may integrate vibration-based and multi-modal sensing, structural dynamics
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), HCP, ABCD, ENIGMA, as well as data from local clinical trials and observational studies (e.g., UMMS EHR data). · Clinical trial design and analysis using advanced statistical methods
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-quality requirements, and analysis-ready data structures. Develop machine learning models that integrate multimodal experimental and simulation data. Contribute to the development of Crucible Data Platform
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or particle Physics, or a related field, A strong background in either nuclear structure, electron-scattering theory/experiment, or global data analysis, Demonstrated expertise in scientific computing and code
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Description Advertised Job Summary An NIH-funded postdoctoral position to work under limited supervision on research examining the effects of dietary patterns on salivary proteome and taste processing. Position
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& Responsibilities Review literature, conceptualize hypotheses, design and conduct experiments, analyze results, and publish in peer-reviewed literature. Build bioinformatic pipelines for analysis of genomic