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include an exciting work in terms of hardware/software use and upgrade of additive manufacturing machines to achieve optimal processing, which could be especially empowered by monitoring and computer
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identity or expression, marital status, military status, national origin, parental status, partnership status, predisposing genetic characteristics, pregnancy, race, religion, reproductive health decision
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intelligence, and multimodal learning. The main objective of this position is to develop novel generative AI methods for computer vision applications, with a particular focus on Diffusion Models and Vision
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 11 days ago
methodology, machine learning, and biomedical data science. Our research develops rigorous and interpretable methods for high-dimensional biomedical data, with applications spanning cancer genomics
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for all postdoctoral scholars appointed through the Office of Postdoctoral Affairs. The FY27 minimum is $79, 056. Appointing a postdoctoral scholar with expertise in causal inference, machine learning, and
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-physical systems security, protection of critical infrastructure, and adversarial machine learning. The position will involve collaborating with faculty and graduate students on interdisciplinary research
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that shape aging trajectories. The project aims to analyze omics data (e.g., genomics, epigenomics, metabolomics, and proteomics) with Mendelian randomization and machine learning methods with longitudinal
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(SHORES) and the Division of Engineering, New York University Abu Dhabi, seek to recruit a Postdoctoral Associate to work on a fascinating project focused on the development machine-learning powered digital
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 16 days ago
joint project between the laboratory “Reproduction et Développement des Plants” at Ecole Normale Supérieure at Lyon (https://www.ens-lyon.fr/RDP/ ), Mosaic team at Inria Lyon (https://team.inria.fr/mosaic
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Union within the project “A Comprehensive Trustworthy Framework for Connected Machine Learning and Secure Interconnected AI Solutions (CoEvolution)”, - CUP F23C24000210006 – selection code: ipd_10D_0426_09/IINF