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Field
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intelligence, machine learning, data science, applied mathematics, or a closely related field, awarded no more than three years prior to the application deadline*. Documented research experience in machine
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developing new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
the group of Dr. Tengfei Li at the University of North Carolina at Chapel Hill. The successful candidate will develop and apply advanced statistical and machine learning methods for infant cognitive
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in University programs, services, and activities. Syracuse University has a long history of engaging veterans and the military-connected community through its educational programs, community outreach
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to contribute to cutting-edge research in robot intelligence, machine learning, and AI-driven manipulation. This position offers the opportunity to work on real-world robotic systems and develop novel algorithms
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project combines techniques from machine learning, natural language processing (NLP), and knowledge representation to support legal scholarship and decision-making. The position entails close academic
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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials
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(7T fMRI, MR Spectroscopy), electrophysiology (EEG), interventional (TMS, tDCS) and neurocomputational (machine learning, reinforcement learning) approaches to understand the network dynamics
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subpopulations, as well as (plastic) cancer cell states that contribute to tumor progression, metastasis and therapy resistance. The candidate will lead several projects applying machine learning to (single-cell
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 3 hours ago
focused on the development and validation of AI- and machine learning–based methodologies for the analysis and interpretation of complex biomedical data. Specific research areas include: Diagnosis and