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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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. - Criterion 2: Knowledge in the scientific areas of the project: Academic or applied knowledge in Software Engineering, Intelligent Systems/Machine Learning, and Interactive Technologies. - Criterion 3
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programming in C (on microcontrollers and embedded Linux); ii. Machine learning on the Edge. Priority will be given to candidates enrolled in a Master Program related to Embedded Systems or related fields
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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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planning and identification of strings and modules in the field. Development of a computer vision and machine learning pipeline for the detection, localisation and classification of defects in photovoltaic
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curricular evaluation purposes, proven skills in programming, preferably Python; machine learning and artificial intelligence; language models and retrieval-augmented generation (RAG); ASR/TTS; diarisation
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applied to healthcare; • To explore approaches suitable for analysing incomplete and/or distributed data, using techniques such as imputation or federated learning; • To adopt good research and development
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possible renewals, a cumulative period of four years of research fellowship intended for doctoral students. Preferential factors: Research experience in the areas of Bioinformatics and Machine Learning
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that are only partially met by the development of special purpose classical computing units. This has motivated a recent interest in using quantum computing to machine learning tasks, in particular to clustering
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29 Jun 2026 Job Information Organisation/Company INESC ID Research Field Engineering » Computer engineering Engineering » Biomedical engineering Researcher Profile First Stage Researcher (R1