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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 23 days ago
, financed by EU and national funds through FCT/MCTES (PIDDAC Workplan: Machine learning algorithms, in particular deep learning ones, suffer from the phenomena of catastrophic forgetting, which hampers
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background and relevant professional experience – 40% Evaluation of academic performance and/or relevant professional experience in machine learning, software engineering, or cybersecurity. Experience in
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16 Feb 2026 Job Information Organisation/Company Instituto Pedro Nunes Research Field Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Positions Bachelor Positions
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-of-the-art models for computer vision based on Machine Learning. Work plan: - Analysis and study of existing resources. - Analysis of the state of the art in universal adversarial attacks on computer vision
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factors: Prior experience in developing algorithms for biomedical image processing (especially aligned with the research group's areas) and machine learning/deep learning techniques. Prior knowledge of data
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-of-the-art models for computer vision based on Machine Learning. - Analysis and Study of existing resources; - Analysis of the state of the art in adversarial attacks and adversarial training and their
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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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“Enhancing Machine Learning Approaches for Spatially Dependent Data in Fisheries and Environmental Research” (CMAT, University of Minho), reference 2024.15617.PEX, financed by national funds through
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relevant areas (e.g., software engineering, cybersecurity, program analysis, machine learning), as evidenced by transcripts. Relevant professional or research experience in software security, static analysis
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(e.g., software engineering, cybersecurity, program analysis, machine learning). Relevant professional experience in software security, program analysis, or AI-driven code analysis. Scientific track