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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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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 3 hours ago
carbon-cycle modeling. The project will build a unified modeling framework that uses GEDI LiDAR and Landsat/HLS data to train deep learning models capable of predicting forest structure variables such as
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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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unique atmosphere where there is expertise to dig deep into computational modelling, while remaining connected to the experimental side. This interdisciplinary atmosphere has been a main catalyst for many
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techniques such as yeast display and deep mutational scanning, or computational candidates with experience in generative AI, reinforcement learning, or agentic AI. The lab is supported by world-class
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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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multidisciplinary team specializing in medical imaging and algorithm development. Our work focuses on advancing the use of computer vision, deep learning, and machine learning for analyzing medical imaging modalities
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computing subjects, including artificial intelligence. In addition, you will be able to demonstrate specialist expertise in one or more of the following areas: - Machine learning and deep learning
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Mathematics, or a related field A strong background in image/signal processing, particularly in computer vision. Strong programming skills and experience with at least one deep learning framework e.g
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) for science with Dr. Aleksandra Ciprijanovic (alexciprijanovic.com) and her research group! The successful candidate will join a multidisciplinary team working at the intersection of deep learning, cosmology