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Posting Details Position Information Position Title Clinical Assistant Professor, School of Social Work Department School of Social Work Posting Number F260033 Posting Link https
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: Computational, Quantitative, and Predictive Modeling of Root Systems. This position emphasizes integration of phenomics and other -omics data into predictive frameworks. Research areas may include: Structural
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., encoder models, multi-modal embeddings, large language models), and mathematical modeling to map scientific concepts and their relationships, tracing how they change, merge, or diverge across time. Your
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, and machine learning models for functional genomics research in mycobacteria. Responsibilities Responsibilities include: Develop and maintain Django-based web applications and databases for sharing
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. TERMS AND CONDITIONS OF EMPLOYMENT Employment of the successful candidate is contingent upon the successful completion of a pre-employment criminal background check. TO APPLY Please apply online at https
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2026. The individual will join a fast-paced team that uses rodent models to study mTOR-pathway related disorders (such as Tuberous Sclerosis Complex - TSC) and autism spectrum disorder (ASD
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scale few others can match. Connections working at The Ohio State University More Jobs from This Employer https://main.hercjobs.org/jobs/22140383/clinic-nurse-endocrine-neuroendocrine-clinic Return
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University of California Riverside School of Medicine | Riverside, California | United States | 3 days ago
-model, peer mentor, and advocate for the Internal Medicine residents. - The PGY4 Internal Medicine Chief Resident position requires a commitment to fulfilling academic and administrative responsibilities
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Transparency). The main tasks will be to: Develop AI-assisted tools leveraging large language models (LLMs) to support community-based fact-checking Designi and evaluate methods to improve the robustness
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associate positions. Dr. Wei Peng's group (www.weipengenergy.com) focuses on modeling institutional and human dimensions of energy transition to identify realistic and robust decarbonization strategies