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the contracting phase, including those resulting from academic degree recognition processes. Preferred factors: In-depth knowledge of Deep Learning and LLMs: practical knowledge with Deep Learning architectures
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academic degree recognition processes. Preferred factors: In-depth knowledge of Deep Learning and Large Language Models (LLMs): practical knowledge with Deep Learning architectures, and in particular, with
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resulting from academic degree recognition processes. Preferred factors: In-depth knowledge in Deep Learning and LLMs: Practice with Deep Learning architectures, and in particular, with Large Language Models
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: Preference will be given to candidates: With a track record of publications related to task planning for robotics. With training and experience in the use of deep learning and large language models
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processes. Preferred factors: In-depth knowledge of Deep Learning and Large Language Models (LLMs): Practical knowledge with Deep Learning architectures, and in particular, with LLMs. Knowledge of both
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, algorithms with a focus on traditional machine learning (shallow learning) and deep learning methodologies. Knowledge of Data Science, including the development of data analysis and visualisation pipelines. 5
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|2025/795 under the scope of the Project Machine Unlearning in Speech Foundation Models: Learning to Forget (LeaF), Refª 2024.14611.CMU , funded Fundação para a Ciência e a Tecnologia, I.P., is now
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Science and Engineering Admission requirements: Knowledge in the areas of data science and deep learning - information provided in the CV and/or motivation letter. Additional optional skills and qualifications
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major research goals are highlighted: (i) the development of deep learning pipelines leveraging longitudinal user health data for knowledge extraction and medical decision support; and (ii
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Prof. João Pereira dos Santos, Assistant Professor 4. Fellowship Activities Plan: Portugal is currently facing a deep housing paradox: more than 723,000 dwellings were declared vacant in the 2021 Census