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employ skills in graphic design, mixed methods research, and scientific communication. Northeastern’s Roux Institute offers an academic research environment combined with access to end-users who bring real
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 months ago
Hiring Range Dependent Upon Qualifications and Experience Proposed Start Date 06/09/2025 Estimated Duration of Appointment 12 Months Position Information Be a Tar Heel! A global higher education leader in
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preservation and climate adaptation? We are seeking a person experienced in interdisciplinary scholarship at the interface of the ecological and social sciences with expertise in mixed-methods approaches
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of RNA polymerase and transcription complexes and/or ribosomes and translation complexes preferred. Effective spoken and written English required. High level of computer literacy required. Experience in
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: Artificial Intelligence / Machine Learning Knowledge Representation and NLP methods Clinical Informatics Bioinformatics Biomedical Ontology Public Health Informatics Nursing Informatics Imaging Informatics
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, or social science. Computer/information scientists with interest in human behavior are also welcome to apply. The successful applicant will have: Excellent research track record. Excellent programming skills
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with mobile health (mHealth) technologies 3. Knowledge of advanced statistical methods (e.g., structural equation modeling, multilevel modeling) 4. Research or clinical experience with individuals with
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, and spatial transcriptomics. Key responsibilities include: Developing AI/ML methods for image alignment across modalities Automated feature detection Predictive modeling of vascularization patterns
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resident at the PGY 2 or PGY 3 level. The applicant must be enrolled in an ACGME-accredited General Surgery program. The estimated annual salary for the position is $73,800. This is an American College
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geographic patterns of dominant water level components over seasonal-to-interannual time scales. Develop methods for quantifying uncertainties in total water level estimations, considering coastal morphology