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Field
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aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one
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Intelligence Climate Change Urban and Territorial Sustainability Water–Energy–Food Nexus Position Description African Research Center on Air Quality and Climate (ARC_AIR) of UM6P invites applications for a
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matter exist in theories beyond the SM, and the WIMP (Weakly Interacting Massive Particle) is one of the best motivated of these candidates, as it allows to also solve the SM hierarchy problem, directly
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Postdoctoral Research Associate in the areas Artificial Intelligence (AI) for Integrated Hydrology Modeling. The successful candidate will have a strong background in computational science, data analysis, and
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permeability assays, murine injury and colitis models, and host-microbe interactions. Key Responsibilities The fellow will: Design and conduct independent and collaborative experiments in gut barrier biology
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starting July 2025, and will remain open until excellent fits are found. The successful candidate will develop and apply computational approaches to chemical datasets, with artificial intelligence/machine
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clustering, redshift-space distortions, weak/strong gravitational lensing, and artificial intelligence/machine learning (AI/ML). The observational focus is on optical sky surveys (DES, DESI, Roman, Rubin Obs
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for greenhouses and vertical farming systems. The successful candidate will focus on robot hardware development, automation system design, machine vision, and intelligent control, supported by physics-based
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Intelligence (CIDSAI) at New York University Abu Dhabi seeks to recruit a Postdoctoral Associate under the supervision of Professor Hanan Salam to work on the development of frameworks, tools, and theoretical
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training pipelines using modern ML frameworks Generating data on miBd–pMHC interactions to guide iterative model optimization, espeicially for specificity Benchmarking AI-designed recognition modules against