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Associate Professor (Tenure Track), The Department of Biomedical Engineering, Faculty of Engineering
of Biomedical Engineering, Faculty of Engineering, Tokyo University of Agriculture and Technology (TUAT) invites applications for the position of Associate Professor (tenure-track) position in Computer Sciences
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human internal state recognition, robotics, XR technology, media processing technology, IoT, and machine learning. - A person who can be actively involved in collaboration with various fields such as
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of the project Electric power, Energy (electrical machines, control, plasma, electric power system, new energy, power electronics, high voltage, and related information technology, etc.) [Work content and job
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contribution to society activity. Teaching responsibilities Graduate level: One or more courses related to the applicant’s specialized fields and Project Based Learning courses from those offered in the master's
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Integration) Course, and Information Creative Course. Students will study information literacy, data science, programming, databases, machine learning/deep learning, and application development. The curriculum
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of the recruitment and description of the project Electrical Engineering (electrical machines, control, plasma, electric power system, new energy, power electronics, high voltage) [Work content and job description
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. The Architecture Course aims to cultivate professionals capable of designing, constructing, and managing excellent buildings and housing by learning architectural technologies, such as design, structure, and
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and seeks to appoint Associate Professor with tenure; Associate Professor/Assistant Professor on Tenure Track System in the research area of Informatics (information security, computer systems, data
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/Particle Astrophysics Particle Physics High Energy Physics Data Science / Machine Learning (more...) Electrical Engineering Appl Deadline: 2025/08/31 11:59PM (posted 2025/06/26, updated 2025/06/22
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significant results by developing rich applications across diverse fields in conjunction with the advancement of modern AI and machine learning. We believe that the theory and methodology of data science are