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of Oxford. The post is funded by United Kingdom Research and Innovation (UKRI) and is for 24 months. The researcher will develop 3D mapping and reconstruction algorithms with relevance to mobile robotics
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dissertated before the start-up date of the position. A research profile with relevant experience in biological sequence analysis, with complementary skills in machine learning or other relevant algorithms. A
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deployment enabling validation and demonstration of real-world applications. For more details, please view https://www.ntu.edu.sg/erian We are looking for a Research Associate to conduct numerical modelling
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input, files/database structures, data transformation, algorithms, and data output by using appropriate computer language/tools to provide technical solution for application development tasks. (Verify
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 4 hours ago
to): Develop machine learning algorithms that utilize fire products from geostationary satellites to better represent fire evolution and variability Develop machine learning emulators to represent forward
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deadlines. A bachelor’s degree in Computer Science or Computer Engineering is required. The candidate should also demonstrate experience and excellence in systems or tool building, algorithmic thinking, and
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similar / - Graduate in industrial engineering or similar - Conocimiento de algoritmos de inteligencia artificial /Knowledge of artificial intelligence algorithms - Dominio de Redes Neuronales
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programme Reference Number AE2026-0003 Is the Job related to staff position within a Research Infrastructure? No Offer Description Portuguese version: https://repositorio.inesctec.pt/editais/pt/AE2026-0003
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of battery modelling and algorithm development, with a strong emphasis on the data-driven modelling and control aspects. You will contribute to shaping the technologies that underpin a more sustainable and
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. Project description This PhD project focuses on advancing the scientific computing foundations of quantum spin dynamics by developing efficient numerical algorithms for modeling complex, open quantum