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intelligence, machine learning, and statistical methods for time-series forecasting in the electricity sector, including demand, renewable generation, and market prices. Development and evaluation of
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, techniques may include hyperspectral imaging, Raman spectroscopy, LIBS and/or XRF, together with calibration, multimodal co-registration, data fusion and machine-learning methods.; The research will
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platform to support the estimation of time, effort, and service planning.; The fellow will contribute to the research, development, and validation of artificial intelligence and machine learning models
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for the quantum imaging system.; - Develop algorithms for spectral feature extraction and for building chemical maps from the data.; - Apply machine learning methods for sample segmentation and
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; - Experience or academic background in machine learning, Graph Neural Networks (GNN)/Grid Foundation Models and/or probabilistic methods and Monte Carlo simulation; - Programming knowledge in Python
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: - Knowledge / experience with the ONNX protocol.; - Experience on the application and tuning of Machine Learning using hardware acceleration.; ; ; Minimum requirements: - Experience with benchmarking tools for
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). Preference factors: - Knowledge of Machine Learning and computer vision (e.g. OpenCV, PyTorch/TensorFlow).; - Knowledge of metaheuristics applied to combinatorial optimization problems.; - Knowledge of