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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | about 1 month 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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' performance will be assessed according to the following weights and criteria: - Criterion 1 - Knowledge in the areas of Bioinformatics, Artificial Intelligence and Machine Learning - Criterion 2 – Motivation
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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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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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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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compression, event-based or neuromorphic vision, signal processing, machine learning or deep learning for visual data. - Motivation for research and scientific dissemination. - Good communication skills in
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; • Organization, systematization, and management of laboratory data. The candidate will also participate in the integration of experimental results with bioinformatics analyses and machine learning methodologies
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hazards, enhancing asset protection, maritime security, emergency preparedness, and societal resilience. The project will leverage advanced AI and machine learning techniques to enable predictive risk
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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