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Field
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Postdoctoral Research Associate will contribute to developing and/or applying artificial intelligence, machine learning, and image segmentation and/or data science-based methods to address cutting-edge topics
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experience. Research background in decision making systems, in particular the use of different optimization, machine learning, and decision making modeling techniques for problem solving. Desire to grow
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ability to analyze large datasets Knowledge of coastal and nearshore processes Preferred Qualifications: Proficiency in statistical modeling and time series analysis Experience with machine learning or deep
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many opportunities to learn new and advanced state of the art techniques and strengthen their grant and fellowship application skills. In addition, the candidate will have a broad range of local and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 13 hours ago
Requirements Applicants should have (or expect to receive before the start of the position) a PhD in Computer Science or EE/ECE on a topic in machine learning applied to natural language processing, computer
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, enhanced by machine-learning and data-driven analysis techniques. Additionally, the study will encompass electrically triggered events that mimic the voltage-based signaling of biological synapses
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models and machine learning. Minimum Qualifications MD or Ph.D. in Basic Science, Health Science, or a related field. No experience required. Preferred Qualifications Experience in epigenetics or gene
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not required as part of this position. Required Qualifications: Strong mathematical background, including expertise in one or more of the following areas: machine learning, statistics, and algorithms
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/machine learning and biomarker detection is a plus. The successful candidate will perform experiments and data analysis, assist with regulatory compliance, write follow-up grants, and disseminate findings
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workers, staff, and faculty to teach and support these workshops. 3. Assist with the development and support of funded DH scholarship, both at USM and at partner institutions across the state, including