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(1-2) Applicants with expertise in one or more of the following areas are encouraged to apply: * Foundation Models * Agentic AI * Reinforcement Learning * Medical Image Analysis Position 2: Intelligent
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statistical and machine learning methods applied to large claims and electronic health record databases and multimodal data, including physiological waveforms and medical imaging. We foster a collaborative and
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(HAI)(link is external) . Research Focus The fellow will lead computational modeling efforts to develop large-scale, multimodal models of the human brain. The work will involve integrating brain imaging
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incandescence, absorption spectroscopy, particle imaging, or light scattering. Experience with extractive measurements such as gas chromatography, mass spectrometry, FTIR, aerosol sizing, particle sampling
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cancer progression in the West lab in the Department of Pathology at Stanford. Successful candidates will use a combination of spatial transcriptomics and highly multiplexed imaging to understand how
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computational biology, cancer biology, and/or molecular biology preferred • Experience in image processing and analysis also preferred • The candidate will report directly to the Principal Investigator and will
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models for image classification, develop habitat suitability models, and evaluate environmental drivers of arbovirus transmission. Our group is validating and utilizing novel approaches to mapping mosquito
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(GitHub) and AI coding assistants (e.g., Claude Code) Strong mathematical foundation relevant to quantitative image analysis (optimization, regression, statistics, signal processing, cluster analysis
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neuroimaging data analysis. Extensive experience with functional magnetic resonance imaging, including resting fMRI and task fMRI. Proficiency in handling NIfTI-format neuroimaging data and performing volume
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primates or humans – Theoretical neuroscience, machine learning, or AI • Proficiency in Python, MATLAB, or equivalent data‑analysis frameworks. • A passion for big‑picture questions, open science, and