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data exploration and processing; Knowledge of Generative AI models n mainly LLM's; Knowledge of satisfaction model analysis. Workplan and objectives to be achieved: The work plan aims at the development
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from academic degree recognition processes. Preferred factors: Knowledge of Machine and Deep Learning; Knowledge in data exploration and processing; Knowledge of Generative AI models n mainly LLM's
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make use of knowledge of data modelling, data storage and data processing. Support in the data modelling component, for the definition of the Domain Reference Model, is required. Work collaboratively and
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Engineering, Engineering Physics, Data Science, or related areas; Preferential factors: Previous experience in modelling and control; Portuguese language proficiency; English language proficiency. Requirement
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algorithms; - Automation of the model customization process by conducting laboratory tests.; - Improvement of the data workflow for real-time processing and sharing.; - Data collection in experimental and real
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the generated data can be used in practice. A new metric to help this comparison is expected to be created. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: Test GAN models – Compare leading GANs
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are:; - Developing energy consumption forecasting tools based on real data.; - Applying these tools to a use case.; - Writing reports and articles for international conferences and journals using the new models and
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the area or area related to that requested in the tender (e.g.: postgraduate studies, advanced studies, specialized training). Preferential factors: Previous experience in Building Information Modelling
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: “GeoMinA – Implementation of a basis for defining geoenvironmental models in abandoned mining areas of the Iberian Pyrite Belt”, reference “PL23-00035”, co-financed by Fundação La Caixa through the Program
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: maintaining laboratory stock materials and inventory as well as provide assistance in purchasing/ordering. • Perform data collection for ongoing experiments, always checking that work has been done accurately