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power systems, network analysis and power flow; - Experience or academic background in machine learning, Graph Neural Networks (GNN)/Grid Foundation Models and/or probabilistic methods and Monte Carlo
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spectroscopy, LIBS and/or XRF, together with calibration, multimodal co-registration, data fusion and machine-learning methods.; The research will involve several main tasks:; • Underwater Sensor Development and
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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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programme Reference Number AE2026-0286 Is the Job related to staff position within a Research Infrastructure? No Offer Description Portuguese version: https://repositorio.inesctec.pt/editais/pt/AE2026-0286
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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
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). Development and validation of machine learning models for calculating occupational health indicators. Integration, management, and analysis of data from wearable monitoring devices. Experimental evaluation
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; - Development of a simulation module for parcel delivery operations, integrating traditional simulation techniques with Machine Learning models; - Development of algorithms for the integration of Machine Learning
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biosignals. Application of machine learning techniques for classification of different classes using the extracted features. Assembly, documentation, testing, and use of an innovative biosensing system