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Earth Observation data analysis and/or spatial modeling Proven ability to publish in high impact peer-reviewed international journals Experience with machine/deep learning / AI applied to environmental
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modeling with deep learning for the analysis of hyperspectral imaging data. The researcher will be responsible for the design and development of numerical models, including neural network architectures
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projects within the CUS related to urban sustainability, environmental monitoring, and urban resilience. Key Duties • Design and implement machine learning and deep learning models for hydrological
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approaches to remove atmospheric particulate (e.g., PM2.5) pollution. The math-based subgroup focuses on the use of deep learning and generative AI to address critical problems for the electric grid and broad
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discipline Strong experience in integrating several of the following components: Deep learning and LLMs for molecular biology Vision foundation models for pathological image analysis Multi-omics datasets (e.g
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
Posting Information Posting Details Department Globl Hlth and Infect Disease - 427801 Posting Open Date 04/10/2026 Application Deadline 04/24/2026 Open Until Filled No Position Type Postdoctoral
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
Posting Information Posting Details Department Globl Hlth and Infect Disease - 427801 Posting Open Date 04/10/2026 Application Deadline 04/24/2026 Open Until Filled No Position Type Postdoctoral
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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description At the heart of SIT’s mission is to nurture industry-ready graduates equipped with deep technical
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POSTDOCTORAL SCHOLAR Requisition # 4568 Position Basics Advertising Ends on:Tuesday, August 18th, 2026 Advertising Started on:Tuesday, March 31st, 2026 College:Carver College of Medicine
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cohorts with rich clinical data. Works on advanced modeling of post tuberculosis lung disease risk using approaches including generalized organoid models, and linear models and deep learning. Minimum