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analysis, kinetic modeling, data analysis, and results dissemination. Qualifications: The ideal candidate will have some prior hands-on experience in preclinical PET/CT or PET/MR imaging, image processing
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Develop computational pipelines and machine learning methods for genomic, clinical, or imaging data analysis. Analyze large-scale biobank, EHR, and/or imaging datasets. Apply statistical, deep learning, and
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. Preferred qualifications Experience in computer vision, medical-image analysis, echocardiography, ultrasound, cardiac CT, ECG, or another clinical imaging modality. Experience with DICOM, PACS, medical-image
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projects. The research associate will develop expertise in retinal imaging, experimental design, data analysis, scientific writing, manuscript preparation, and presentation of research findings, supporting
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). Maintain rigorous experimental documentation, data management practices, and reproducible analysis pipelines. Present results at lab meetings, departmental seminars, and scientific conferences; contribute
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cover letter describing research interests, previous experience in in vivo two-photon imaging and data analysis. Contact information for three references who can comment on research experience and
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histological and multiomic analysis of MS brain tissue to elucidate cellular and molecular mechanisms underlying demyelination and neurodegeneration. The laboratory is embedded within a highly collaborative
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include: Understand the extent of existing digital images of text, and how the information locked away in them would advance provenance understanding Acquire additional external data, such as the Getty
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, including spatial transcriptomics In vivo imaging of neural circuit deficits in zebrafish Pharmaco-behavioral profiling to identify pharmacological candidates Key responsibilities Analysis of molecular
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paradigm, and an NIMH study focuses on depression and anxiety genetics in large biobank samples. Through these and several other current projects, we have access to large datasets, including the Million