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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 advanced prompt
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Area: Computer Science 2. Admission Requirements: Graduates (Licenciatura) in computer engineering or related area, with experience in Machine Learning/Deep Learning methods/techniques. 3. Project
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those resulting from academic degree recognition processes. Preferred factors: In-depth knowledge of Deep Learning and LLMs: practical knowledge with Deep Learning architectures, and in particular, with
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those resulting from academic degree recognition processes. Preferred factors: In-depth knowledge of Deep Learning and LLMs: practical knowledge with Deep Learning architectures, and in particular, with
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those resulting from academic degree recognition processes. Preferred factors: In-depth knowledge of Deep Learning and LLMs: practical knowledge with Deep Learning architectures, and in particular, with
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those resulting from academic degree recognition processes. Preferred factors: In-depth knowledge of Deep Learning and LLMs: practical knowledge with Deep Learning architectures, and in particular, with
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for applications is required, in the contracting phase, including those resulting from academic degree recognition processes. Preferred factors: In-depth knowledge of Deep Learning and Large Language Models (LLMs
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completed by the deadline for applications is required, in the contracting phase, including those resulting from academic degree recognition processes. Preferred factors: In-depth knowledge of Deep Learning
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systems where the candidate played an active role together with familiarity with deep learning methods. EVALUATION CRITERIA The selection will be based on the following criteria: CV: 50% Experience in
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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