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of Energy (DOE) experimental facilities. This role involves research and development spanning areas such as optimization, Fourier analysis, numerical linear algebra, statistics, machine learning, and high
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statistical modelling, medical imaging data analysis, cancer omics, precision medicine and translational statistics. Depending on their profile, the successful candidate will work on one of the following
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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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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 21 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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strong technical expertise in deep learning, such as models for image segmentation, classification, multi-modal processing, foundation models, or agentic frameworks. An extensive background in computer
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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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Characterization Facility to develop and employ novel techniques for defect characterization in solids. Core responsibilities include: Core Responsibilities: Expert on modalities in the scanning electron microscope
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against oceanographic variables to inform management actions to support the recovery of the endangered SRKWs. Multi-modal datastreams will further be used to develop a unified statistical detection model
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with environmental/scientific datasets (cleaning, processing, analysis, synthesis). Strong programming skills, especially Python (or comparable scientific programming). Experience with LLM-assisted