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knowledge of machine learning models and Python tools for signal processing and machine learning. General knowledge of system architecture and APIs. Previous knowledge of physiological signal processing. 5
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knowledge of deep learning architectures and, in particular, Large Language Models (LLMs). Knowledge is valued both in advanced prompt engineering techniques (e.g. few-shot learning, chain-of-thought
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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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insurance, supported by INESC TEC. 2. OBJECTIVES: • Research novel deep learning models for anatomically structured EGGIM estimation.; • Develop methods for image-level and examination-level reliability
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models to characterize lung cancer based on a non-invasive methodology. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: - extend the knowledge of the state of the art in machine learning
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supporting documentation, proven experience in all of the following areas: Computer vision and video processing (ingestion, ROI, 2D/3D keypoints, heatmaps); Deep learning and temporal modelling (CNNs
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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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; • Knowledge of data analysis, Artificial Intelligence or machine learning; • Knowledge of energy systems and modelling of energy resources. 5. EVALUATION OF APPLICATIONS AND SELECTION PROCESS: Selection
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; Skills in computer programming. Workplan and objectives to be achieved: Development of Machine Learning tools for retrobiosynthesis pipelines, including: (i) validation and extension of models and tools
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://www.di.ubi.pt ), under the following conditions: Research Field: Machine Learning/Pattern Recognition Objectives: Foundational Models for Human-Machine Interaction Work plan: The work consists in develop a system