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backgrounds in biomedical informatics, computer science, machine learning, statistics, data science, computational biology, or related quantitative disciplines. Experience with longitudinal health
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technical expertise in machine learning, artificial intelligence, and data modeling approaches. We are committed to investigating the mechanisms of brain aging and developing innovative interventions to
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large‑scale systems neuroscience with machine learning to derive principled models of cortical computation. Our newly refurbished primate facility, state‑of‑the‑art Neuropixels rigs, and high
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cardiac surgery outcomes research and predictive analytics using advanced statistical and machine learning methods applied to large claims and electronic health record databases and multimodal data
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density in regulating these processes. The insights gained from these empirical studies will be paired with computational biology and machine learning to develop predictive AI models of how cells
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, machine learning, computer science, statistics, engineering, medicine, or a related field. Strong candidates may have experience with large-scale human datasets, machine learning, statistical genetics
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is geared for a recent PhD graduate with interest in collaborating across disciplines, and expertise in remote sensing, spatial data analysis, machine learning and computer vision. The position will be
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detection, waveform segmentation, feature extraction, hemodynamic modeling, time-series analysis, machine learning, and development of algorithms for real-world clinical data. The fellow will have the
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. Developing the research program will include supporting the team with quantitative, inferential and spatial analysis, as well as integrating machine learning techniques, and leading and drafting manuscripts
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the tool. Mentorship and Collaboration Dr. Philip Fisher will serve as the official faculty mentor for this fellowship. The fellow will also work closely with SCEC Senior Machine Learning Engineer Dr