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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 4 hours ago
Dependent on Experience and/or Qualifications Proposed Start Date 10/19/2026 Estimated Duration of Appointment 12 Months Position Information Be a Tar Heel! A global higher education leader in innovative
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be applied. The approximate amount of salary at the time of employment: The total amount of monthly discretionary work allowance and monthly salary at the time of hiring will be 382,610 JPY or more
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intelligence. Our research spans computer vision, machine learning, and natural language processing, focusing on multimodal learning, data fusion, spatial-temporal modeling, and vision–language models. We study
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to work collaboratively with others and contribute to a team environment. Technical Proficiency: Skilled in using office software, technology, and relevant computer applications. Communication: Strong and
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. Discretionary work, commuting and housing allowances will be provided. Social insurance will be applied. The approximate amount of salary at the time of employment: Postdoctoral Researcher: The monthly salary
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this case, to coastal dunes, wind flow dynamics and wind-driven sand transport. You have an open, collaborative and curious attitude. You enjoy exploring new approaches and you combine this with
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safeguards University assets. CORE QUALIFICATIONS Education: Ph.D. in relevant field Certification and Licensing: Not Applicable Experience: No prior experience required Knowledge, Skills and Attitudes: Skill
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funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Leverage computer vision, smart
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collaboratively with others and contribute to a team environment. Technical Proficiency: Skilled in using office software, technology, and relevant computer applications. Communication: Strong and clear written and
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gliomas. The project integrates generative AI, interpretable models, uncertainty estimation, and federated learning to improve clinical decision-making, data privacy, and personalized medicine