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understand plasmadynamics and hypersonic flow physics, and 2) the exploration of transformational concepts for in-space power and propulsion using innovative laser architectures and/or rarefied atomic beams
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for complex scientific problems Designing algorithms to improve the performance of scientific applications Researching digital and post-digital computer architectures for science Developing and advancing
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multimodal data, dynamic updates, and scalable semantic interoperability in large-scale DPP systems. Particular emphasis will be placed on Representation Learning techniques, Transformer-based architectures
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architectures for science Developing and advancing extreme-scale scientific data management, analysis, and visualization Developing and advancing next-generation machine learning, AI, and data science approaches
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knowledge for the future and work hard for the development of society. We create space for brilliant research and inspire creative advancements in technology, architecture and design. We have nearly 10,000
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subjects and in the following Arts/Humanities disciplines: Anthropology; Architecture; Modern and Medieval Languages (French, German, Italian, Spanish, Russian, Portuguese); Classics; Education; History
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architectures”. Qualifications For the post of Postdoctoral Fellow, applicants should have a doctoral degree or an equivalent qualification and must have no more than five years of post-qualification experience
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to work on Liaise with co-I from NetaTech and Tohoku University to apply the in-house developed light conversion film on the architectural design and system used for the UmFm. Taking care of the day to day
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: Proficiency in Python and major deep learning frameworks such as PyTorch or TensorFlow Familiarity with transformer architectures and large language models (e.g., BERT, GPT) Experience in building, training
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physics, and quantum error mitigation. Good programming skills in Python. Familiarity with PyTorch or tensorflow will be a bonus. Proficiency of programming skill with quantum machine learning architectures