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LiDAR data (GEDI and ICESat-2), and generate time series; e) Apply deep learning (DL), machine learning (ML), geospatial foundation models (e.g., AlphaEarth, TESSERA), statistical inference (uncertainty
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Area: Computer Vision Group: Pattern and Image Analysis Work Objectives: In terms of deep learning architectures for object detection, particular attention will be given to the analysis of performance
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 2 months ago
Europe Research and Innovation Programme under the Grant Agreement No.101178775 Workplan: Design, develop and implement deep learning methodologies for generation of subsurface earth models. Duration
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on artificial intelligence techniques, namely machine learning and deep learning; (3) analysing mathematical models applicable to renewable energy generation technologies and electrical energy storage systems; (4
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 3 months ago
of the trajectory. This grant targets the development and rigorous benchmarking of novel, energy-efficient CAD-based deep learning architectures that fuse visual and inertial modalities for the robust 6D pose
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paper, including the previous developments, should be published and our group established as one of the players in the area of deep-learning for computational mechanics. Legislation and Regulations
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requirements: Experience using deep-learning algorithms. In-depth knowledge of Python and PyTorch. Previous experience collaborating on scientific projects. Publications on deep-learning topics. 4. Work Plan
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Infrastructure EU-OPENSCREEN. A web server will be developed for predicting modes of action of small molecules based on a multimodal deep learning model. Workplace and Scientific Guidance: The work will be carried