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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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, vegetation occlusion, and low illumination conditions; - Contribute to the definition of resilient perception architectures for outdoor robotic operation. 2. Machine learning-based resilient perception
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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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segmentation, normalization, and feature extraction in the time, frequency, and time-frequency domains. 4) Development and training of machine learning and deep learning models (such as SVM, Random Forests, CNN
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environment. 4) Development of machine learning and deep learning models for forecasting reduced visibility, low cloud ceilings, and adverse weather conditions impacting air operations. 5) Implementation
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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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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