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of the recruitment and description of the project Next-Generation Intelligent Information Processing Fields (machine learning, optimization, control, simulation, bioinformatics, materials informatics, energy
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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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of the project] * Background of the recruitment and description of the project Numerical Analysis (Numerical Algorithms), Mathematical Optimization, Operations Research, Machine Learning, Mathematical Statistics
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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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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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information theory, thermodynamics, and machine learning. In particular, our team focuses on non-equilibrium open quantum systems and quantum thermodynamics, with a central emphasis on exciton Bose–Einstein
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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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Engineering Cosmology/Particle Astrophysics High Energy Physics Data Science / Machine Learning (more...) Particle Physics Appl Deadline: 2025/11/30 11:59PM (posted 2025/06/26, updated 2025/06/22, listed
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Electrical Engineering Nuclear Physics High Energy Physics Data Science / Machine Learning (more...) Particle Physics Appl Deadline: 2025/11/30 11:59PM (posted 2025/06/26, updated 2025/06/22, listed until
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simulation or machine learning of soft and biological matter, e.g. Monte Carlo, molecular dynamics and finite element computer simulations. Origins of life research involving topology and chirality