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and solve them with various engineering approaches.Our laboratory aims to design faster and more scalable quantum computers by exploring their systems and software, as modern computer engineering
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Surface (RIS) Nonlinear circuits Millimetre wave Electromagnetic simulation We also welcome researchers from different fields: AI (machine learning, big database, etc) Semiconductors As a minimum
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. [Work content and job description] (Immediately after hiring) Construction of a machine learning model describing the relationship between input data (such as solar cell structure, fabrication process
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sciences. At the time of appointment, it is not necessary for applicants to be machine learning experts. We welcome candidates who have established expertise in fields such as physics, neuroscience
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an objective, scientific approach based on prediction to the sustainable development of human society. [Keyword] Data Assimilation, Prediction, Machine Learning, Simulation, Statistical mathematics For further
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technology and develop human resources. The AI Computing Team explores the design and realization method of advanced machine learning systems by working across multiple layers, including circuits, devices
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Seeking a Research Scientist or a Postdoctoral Researcher (25-1291)（Sequential Decision Making Team）
to sequential decision-making, such as online learning and reinforcement learning. The candidate is expected to carry out research and publish their findings in top machine-learning conferences, and help in
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mathematical information science approaches, such as scientific machine learning. Potential research topics include, but are not limited to: (1) Bayesian estimation of 3D velocity structure models using ocean
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; identification of novel phases of matter through machine learning; and the development of new algorithms for the simulation of quantum matter. Applications from strong candidates with complementary interests
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position) Designing a machine-learning-based bias correction method using retrospective forecasts and reanalysis data for comparative calibration. Topic 3: Development of seasonal prediction models (one–two