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Field
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Requirements: PhD degree in Artificial Intelligence, Computer Science, or a related field from a prestigious institution. Good written and oral communication skills. Proficiency in developing deep learning
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-disciplinary AI research and learning. Incorporates advanced methodologies such as deep neural networks and language/multimodal models, leading the preparation of scientific papers and technical reports
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languages; A good record of publications in reputable peer-reviewed journals or conferences in maritime transport, logistics management, machine learning, deep learning, and optimization; Proficient in
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, computational, and machine learning/AI methods, with a particular emphasis on deep learning approaches improve our understanding and prediction of infectious disease dynamics. Projects are also strongly grounded
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deep learning. Strong analytical skills, a track record of publication in high-impact journals, and an ability to work collaboratively within a multidisciplinary team is essential. Key Responsibilities
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should be highly proficient with a range of machine learning approaches, including unsupervised, semi-supervised, supervised, and various deep learning techniques (e.g., convolutional neural networks
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and synthesis of novel materials. Key Responsibilities: Develop and apply generative AI models for materials discovery, leveraging deep learning, Bayesian optimization, and active learning. Integrate
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Learning-related field. Programming experience in Matlab, Python, C++ or other relevant language and experience in deep neural networks. Experience and demonstratable knowledge in deep learning, transformer
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looking for your next challenge? Do you have a background in machine learning or fluid dynamics and an interest in applying your skills to understand the dynamics of Earth’s fluid core and space-weather
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Science, Engineering or other Machine Learning-related field. Programming experience in MATLAB, Python, C++ or other relevant language and experience in deep neural networks. Experience and demonstratable knowledge in