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, machine learning, and statistical methods for time-series forecasting in the electricity sector, including demand, renewable generation, and market prices. Development and evaluation of point and
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the European Union through the COMPETE 2030 Programme, of Portugal 2030, under the following conditions: Scientific Area: Machine Learning Admission requirements: Candidates who cumulatively meet the following two
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HIL environment rather than only on offline simulation. Learning outcomes anticipated include stronger understanding through immediate feedback on live systems, deeper engagement with
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and sustainability; Investigate and apply artificial intelligence and machine learning techniques, including large language models (LLMs), across CENSE’s scientific body in its five thematic areas
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the PORTUGAL2030 Programme, under the following conditions: Work Plan and Objectives to Reach: The work to be carried out aims at the research and development of Computer Vision and Machine/Deep Learning algorithms
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of the application deadline. Preferred factors: The selection will value candidates with interest and/or experience in the following areas: a) Demonstrated experience in machine learning and deep learning techniques
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, and/or artificial intelligence and machine learning. Familiarity with analog, mixed-signal or RF integrated circuit design and simulation tools will be considered an advantage. Good analytical and
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, including electrocardiograms and other wearable sensors, for subsequent application of machine learning and deep learning methods and classification of health and wellness parameters. Data acquisition, as
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sensors, for subsequent application of machine learning and deep learning methods and classification of health and wellness parameters. Data acquisition, as well as the preparation of presentations
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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