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Field
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Education and/or Work Experience PhD required in related field. Qualifications Minimum Qualifications: Earned doctorate in Research Methodology, Social Work, School Psychology, Counseling Psychology
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for genomics (e.g., generative models, transformers, agentic workflows) and/or statistical learning (e.g., network & spatiotemporal modeling, functional/longitudinal data, time-series). Analyze single-cell
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informatics, electrical engineering, or a related field Demonstrated expertise in machine learning, deep learning, and image-based modeling. A strong publication record in top-tier venues (e.g., MICCAI, CVPR
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
Engineering stresses the application of science and engineering, mathematical analysis, and computer techniques to biomedical problems. The research and entrepreneurship of the faculty, students, and staff aim
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AI transforms civic life, democratic practice, and public reasoning. The AIDC Initiative brings together scholars from the humanities, social sciences, and technology studies to examine how AI reshapes
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Research Centre. Mōu | Who You Are To be successful in this position, ideally you will have: A PhD in Computer Science or a closely related field Expert knowledge of deep learning, large language models
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must possess substantial experience in artificial intelligence and machine learning methods, specifically in AI-driven materials discovery, machine learning applications for materials, or generative AI
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environment. Technical Proficiency: Skilled in using office software, technology, and relevant computer applications. Communication: Strong and clear written and verbal communication skills for interacting with
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Position Description Academic Job Description Department of Biological and Environmental Engineering Postdoctoral Associate – Biotechnology The College of Agriculture and Life Sciences (CALS) is a
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of the following: computational analysis of human movement and/or vocalization, time series analysis, pattern finding, statistical modelling, audio feature extraction, music information retrieval, machine learning