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supporting documentation, proven experience in all of the following areas: Computer vision and video processing (ingestion, ROI, 2D/3D keypoints, heatmaps); Deep learning and temporal modelling (CNNs
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insurance, supported by INESC TEC. 2. OBJECTIVES: • Explore machine learning approaches for discovering interpretable and clinically relevant visual representations.; • Validate the proposed methodologies
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candidates using photometry, spectral energy distributions and, when available, spectroscopic data. Both fellows will produce diagnostic plots and summary tables. Simple statistical or machine-learning methods
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, machine learning, programming, software engineering, instrumentation, benchmarking, reproducibility and technological prototyping. The mandatory requirement for technical-scientific proficiency in English
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-resolution or single-molecule imaging. ● Experience with machine learning/deep learning for bioimage analysis, including model application, validation and/or development. ● Experience with optical development
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, decentralized authentication, trust management, secure communications, Artificial Intelligence-based intrusion detection, and adversarially robust machine learning techniques.; ; Furthermore, the fellowship seeks
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Center, Corporate Partnerships, Alumni, Intellectual Property, Entrepreneurship). This grant offers the fellows the opportunity to engage in scientific activity and learning within the various areas
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supporting documentation, proven experience in all of the following areas: natural language processing and machine translation (sequence-to-sequence modelling, NMT, glosses); deep learning, Transformers, and
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Vineeth Surendra, taking place at the {////} sins-lab of the University of Beira Interior (https://sins-lab.ubi.pt/ ) under the following conditions: Research Field: Computer Engineering Objectives
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of ecosystem degradation and the aforementioned extreme climatic phenomena; (v) explore a “Machine Learning” analysis to explore the importance of other environmental and managerial factors in the spatial and