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Position Details Position Details Job Title Post-Doctoral Research Position Number 8151040 Work Modality Fully In-Person Is this request for the creation of a new Position (or the modification
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 20 days ago
the development and implementation of clinical trials, including feasibility testing, mechanism evaluations, advanced quantitative and qualitative methods, systematic reviews/meta-analysis, and implementation
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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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. The activity of the IMAGES team of the IDS department in LTCI covers many aspects of the processing, analysis and synthesis of digital images, volumes, and videos. A particularity of the team’s work is
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modalities. The candidate will have the opportunity to work on “big data” studies in health and diseases, including Alzheimer’s disease and others such as schizophrenia, psychosis, autism, etc. We collaborate
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(https://www.nature.com/articles/s43247-021-00105-x#Ack1 ; https://www.frontiersin.org/articles/10.3389/fmicb.2019.01492/full ). We aim to investigate the ecological distribution of metallophilic
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Post Doctoral Researcher Rinn Artificial Intelligence – Research & Innovation in Data Science and AI
. The host research group leads a number of projects on the development of theoretical and applied methods in statistical modelling, medical imaging data analysis, cancer omics, precision medicine and
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an advantage Strong background in machine learning for image analysis and computer vision, ideally involving microscopy, time-lapse imaging, or other high-dimensional scientific imaging modalities
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bioinformatic analysis of the resulting datasets. You will work to integrate proteomic data with orthogonal single-cell modalities including transcriptomics and imaging, and collaborate closely with clinical and
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machine learning models. Working with extremely large, multi-modal datasets. Prior experience in analysis of clinical health records, and time series data are highly preferred. Qualifications Requirements