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projects within the CUS related to urban sustainability, environmental monitoring, and urban resilience. Key Duties • Design and implement machine learning and deep learning models for hydrological
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deep learning, on the topic “Analysis and Reconstruction of Digital Data Fragments”. This internship is intended for students at M2 level, or in the final year of an engineering school, interested in a
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and adapt machine learning and deep learning models (e.g., convolutional and transformer-based architectures) to biological questions in collaboration with investigators. Develop interpretable models
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modeling with deep learning for the analysis of hyperspectral imaging data. The researcher will be responsible for the design and development of numerical models, including neural network architectures
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The VCC center at KAUST is looking for research scientists in Prof. Wonka's research group. The topics of research are computer vision, computer graphics, and deep learning. A suitable candidate
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on conventional yellow sticky traps. MANIFESTO team will explore advanced and modern Deep Learning (DL) architectures to develop an accurate model for detection and classification. Moreover, the project aims
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Venturelli Lab The Venturelli Lab at Duke University (www.venturellilab.org) is seeking highly motivated researchers or postdoctoral researchers with expertise in machine learning, deep learning, and/or
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Department of Energy and Environmental Protection (CT DEEP), related to air emissions, regulated building materials, water and land use, and water discharges and provide guidance to project teams, facilities
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Alabama and collect pilot data to support larger subsequent research proposals. A carefully assembled team of mentors with deep experience in mentoring and collective expertise across all of the domains
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Data understanding Software development of automatic writer/hand identification And will allow you to develop competences in Software management (e.g., Git use) Data preparation to train Deep Learning