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of artificial intelligence and behavioral neuroscience. This individual will join our interdisciplinary team of computer scientists and behavioral neuroscientists seeking to identify the mechanistic and
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and Machine Learning tools and algorithms to solve hydrology and water resources problems. Familiarity with high-performance computing (HPC), cloud platforms, or GPU clusters. Demonstrated ability
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intelligence and machine learning Food is Medicine policy analysis, evidence synthesis, and simulation modeling, including impacts on health, costs, and cost-effectiveness Strengthening nutrition security in
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Qualifications* PhD in Computer Science or Engineering, Biomedical Engineering, Neuroscience, Bioinformatics, or other relevant field. Experience with machine learning and statistical analyses. Proficiency with
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with a PhD in computer science or bioinformatics are encouraged to apply. We create statistical, machine learning, and deep learning approaches for the processing of this data, with a major focus on
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Tool (SWAT model), and/or Noah Land Surface Model with Multi-Parameterization Options (Noah-MP model). Experience in either developing or applying Artificial Intelligence and Machine Learning tools and
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contribute to overall lab operations. The applicant will be a collaborative, impact-focused problem solver who wants to be part of a dynamic team. Learn more about the innovative work led by Dr. Don Ingber
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genome editing, CRISPRa/i Animal work and rodent surgeries Machine learning / artificial intelligence (using imaging or 'omics data) Responsibilities: Lead independent and collaborative projects; establish
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one or more of the following areas: (1) modeling of infectious disease dynamics, (2) statistics, machine learning, and AI, or (3) operations research and optimization. Preference will be given
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 11 hours ago
be predicted using machine learning based on drug-specific information, patient demographics, and clinical trial data. 2. Modeling for Regulatory Science – Leveraging drug development and regulatory