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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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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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courses of Higher Education Institutions. Preference factors: • Fluency in English and Portuguese, spoken and written; • Experience in data analysis, Artificial Intelligence or machine learning
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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
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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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. - 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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of Artificial Intelligence (30%); iii) knowledge of advanced methods for machine learning, such as online or reinforcement learning (20%). Applicants whose application is scored with a final classification of
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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