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Field
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image analysis packages such as Freesurfer, FSL, SPM, or 3DSlicer, or using machine learning or artificial intelligence models would be advantageous What We Offer The appointee would be exposed to ample
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intelligence, data science, medical physics, neuroimaging, bioengineering, or related disciplines, accompanied by accredited training in machine learning, deep learning, or medical image analysis. Experience: A
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fingerprinting (RFF) systems using machine learning. The successful candidate will have significant experience in signal processing or machine learning, and an outstanding track record in conducting research. You
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. Education and scholarly development The postdoctoral associate will receive structured education in computer vision applications in medical imaging, machine learning, research methodology, responsible conduct
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Title: Postdoctoral Research Associate|Machine Learning/Artificial Intelligence for Additive Manufacturing Employee Classification: Postdoctoral Research Assoc Campus: University of North Texas
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(or similar): Coherent diffractive imaging, especially ptychography. Sparse sensing, optimization, or Bayesian experimental design. Machine learning for imaging. Synchrotron experiment experience. Semiconductor
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echocardiography dataset called CAIFE consisting of both healthy and abnormal fetal heart scans. You will be responsible for the design and testing of original machine-learning based methods for fetal heart
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(or similar): Coherent diffractive imaging, especially ptychography. Sparse sensing, optimization, or Bayesian experimental design. Machine learning for imaging. Synchrotron experiment experience. Semiconductor
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. in Computer Science, Electrical Engineering, Biomedical Engineering, or a related field, with expertise in computer vision or medical image analysis. 2. Demonstrated research experience in 2D/3D
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analyse electrochemical free-energy landscapes and connect elementary-step energetics to catalytic performance apply machine-learning interatomic potentials and microkinetic models where appropriate