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, electrical engineering, cognitive science, applied mathematics, physics, or a related field. Strong computational background and hands-on experience building AI/ML models. Expertise in modern architectures
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, observations, a hierarchy of numerical models, and machine-learning methods to understand their formation, dynamics, and predictability. The successful candidate will have substantial freedom to develop
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prognostic markers for psychosis in youth at clinical high risk, using state-of-the-art AI models and multimodal neuroimaging, clinical, and cognitive data. The position will emphasize advanced computational
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on physiologic waveform analysis, biomedical signal processing, and computational modeling of continuous clinical monitoring data. The successful candidate will work on projects involving the analysis
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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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care for patients requiring urgent or emergent intervention. The fellowship provides comprehensive training in data engineering, exploratory analysis, statistical modeling, machine learning, and artificial
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campus as arts and sciences. The fellow will lead the development and validation of imaging-based models to predict patient response to cancer treatment (80%) and will manage the unit’s AI/ML core
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modeling of biological systems. One major effort in the lab is the µDicer platform (https://www.nature.com/articles/s41378-024-00756-8(link is external) ; https://www.biorxiv.org/content/10.64898