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Field
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processes relevant to human health and disease using using genetics, molecular biology, biochemistry, cell biology, and microscopy/imaging. A qualified candidate will be capable of working independently
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Education and Experience Preferred experiences include research in cognitive neuroscience, human functional brain imaging, psychology, neurology, psychiatry, machine learning, data visualization, and data
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molecular biology skills; proficiency with standard word, statistical, and imaging processing software (e.g., Microsoft Word, GraphPad Prism, Adobe Illustrator). Job Duties Job Duty Implement genetic, cell
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-reviewed publications. Prior experience with biological network analysis and practical application of a variety of machine learning and computer vision techniques is preferred. The successful candidate will
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collaboration with theorists, machine learning experts, and other experimentalists. Essential Duties and Responsibilities: Single crystal growth of quantum materials Performing transport and electron microscopy
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management and coding, machine learning AI, and familiarity with LSST data management image reduction and analysis tools. Familiarity with processing data at the DOE US Data Facility at SLAC National
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relevant field at the PhD level with zero to five years of employment experience. Experience with deep learning frameworks (PyTorch, TensorFlow, JAX). Strong background in computational image processing and
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translational research involving multimodal neuroimaging analyses, statistics, machine learning, and/or glucose metabolism are preferred. Education and Experience Requirements: PhD in neuroscience
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longitudinal modeling, machine learning methods, subgroup analysis, or other advanced modeling techniques is highly desirable. Software Proficiency: Experience with neuroimaging tools such as AFNI, SPM, FSL
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. The appointee will primarily conduct research applying advanced machine learning/AI (including techniques like deep learning) to analyze complex biological and clinical data (e.g., single-cell multi-omics