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knowledge-grounded reasoning with flexible machine learning Tools that reduce manual burden while preserving traceability and clinical interpretability This position offers the opportunity to publish novel
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assistants, and large language models. Areas of interest for possible collaborations include but are not limited to: topological data analysis and topological machine learning; AI-assisted theorem proving
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applications for a post-doctoral research position under the supervision of Dr. Chris Smith Home | Chris Smith . The lab— in the Evolution, Ecology, and Behavior section—investigates machine learning approaches
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experimental disease models to identify disease mechanisms, discover therapeutic targets, and improve disease prediction, prevention, and treatment. Our research focuses primarily on substance use disorders and
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management. Artificial Intelligence & Machine Learning: Predictive risk modeling for falls, hospitalization, and cognitive decline; Natural Language Processing (NLP) for remote cognitive assessment; image
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Science, Computer Science, Data Science, Neuroscience, or a related field by the start date. Demonstrated expertise in computational modeling of human behavior or computer vision / machine learning
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pay-per-click model. • Customized worklists, dictation templates, and AI programs such as Rad AI, which auto-generates impressions. • 24/7 reading room clinical administrative assistant support for
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mouse models is required. Experience in bone and liver biology or willingness to develop an interest in inter-organ crosstalk are preferred. Required skills: Basic computer skills and proficiency with
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Informatics, Health Data Science, Biostatistics, or a closely related area. Strong ML/deep learning foundation plus expertise in at least one of: multimodal learning, time-series modeling, or NLP. Demonstrated
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knowledge-grounded reasoning with flexible machine learning Tools that reduce manual burden while preserving traceability and clinical interpretability This position offers the opportunity to publish novel