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transportation systems and autonomous driving. • Strong understanding of generative AI, deep learning, and multimodal machine learning, with hands-on experience. • Excellent programming skills and proficiency with
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, KELIM score), mutational profiles, histopathological information, and long-term survival outcomes. The first objective is to implement automated deep-learning–based segmentation of primary ovarian tumors
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to the development of state-of-the-art AI approaches applied to land surface monitoring, particularly using satellite observations. These approaches may include machine learning and deep learning methods
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development. Job Details The successful candidates are expected to: • Conduct research in computer vision and deep learning, including literature review, hypothesis formulation, and experimental design
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of Informatics. You will be part of Visual Intelligence and the DSB group. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/293458/phd-research-fellow-in-deep-learning
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Area: Computer Vision Group: Pattern and Image Analysis Work Objectives: In terms of deep learning architectures for object detection, particular attention will be given to the analysis of performance
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scoping to deployment and monitoring of production-grade models—with a focus on both Generative AI and Deep Learning. The ideal candidate holds a Ph.D. in Deep Learning or Generative AI and brings a strong
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 1 hour ago
. Description: This project aims to develop a next-generation wildfire risk assessment platform that tightly integrates Earth Observation (EO) data, deep learning, and dynamic fire behavior modeling
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. About You You’re a senior Engineer with a wealth of experience in enterprise AV technologies, who loves untangling complex challenges, leads with deep technical expertise, and takes pride in delivering
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architectures for TTS and ASR Entrenamiento de modelos a gran escala utilizando frameworks modernos de deep learning / Training large-scale models using modern deep learning frameworks Publicaciones en