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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 18 hours ago
integrating deep learning with physics-based modeling, implementing scalable training strategies, and validating methods on both simulated and experimental datasets. The role also involves close collaboration
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informatics, or a related field - Strong programming skills in Python and experience with deep learning frameworks (PyTorch preferred) - Experience or strong interest in large language models, multimodal
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Classification Title: Assistant/Associate/Full Professor Classification Minimum Requirements: a Ph.D. in computer science or related field Expertise in AI/ML, deep learning Expert proficiency in
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requires a deep understanding of the solvents involved, such as highly concentrated aqueous or non-aqueous electrolytes. Accurate modeling of these systems relies specifically on the knowledge
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related fields. Additional optional skills and qualifications: Experience in deep learning for medical imaging. Contracting requirements: Presentation of the academic qualifications and/or diplomas
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application! We are looking for a PhD student in biomedical engineering with a focus on deep learning for medical images Your work assignments The position focuses on developing methods for federated learning
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To find out more about this role, including details of how to apply, please visit quoting reference 7990/1 https://plusportal.perrettlaver.com/VacancyDetail/90f9745c-253c-69a7-4d92-3a1fa2a0e672 Proud of our
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básica en análisis de datos. Programación en Python. Conocimiento de modelos de machine y deep learning. Nivel medio de inglés. Secondary school diploma, vocational training (FP), or Bachelor’s degree
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, and who are eager to contribute to impactful methods for generating private and fair synthetic data with good utility. This project involves development of deep learning based synthetic data generators
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at the intersection of mathematics and AI safety, with a focus on developing rigorous mathematical foundations for AI interpretability. Research directions include mean field theories of deep learning, data attribution