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(pre-processing, filtering, feature extraction in the time, frequency, and time-frequency domains). Development and validation of machine learning and deep learning models; integration and analysis
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, storage, and local electricity grids. A key goal is to translate methodological innovations in deep learning into practical tools for sustainable urban energy systems, supporting applications in forecasting
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into actionable insights, novel tools, and impactful research outcomes. Key Responsibilities Develop, implement, and optimise AI/ML models (artificial intelligence/classical machine learning, deep learning
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., turbine components).Research on advanced deep learning techniques, including architectures based on GRU, LSTM, attention mechanisms, and hybrid models.Implementation of real-time predictive models (soft
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Job Requirement Have relevant competence in the areas of Deep Learning/Computer Vision. The experience in diffusion models is a plus. Have a PhD degree in computer science/engineering or related
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Engineering, Mechatronics, Computer Science, etc. Strong background in AI, Large Language Model, autonomous driving, deep learning, interaction modelling, prediction, robotics and automation. Candidates having
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interested in using AI to unravel the mysteries of the brain? Do you want to perform cutting-edge NeuroAI research and leverage deep learning to understand human vision? Then check out the vacancy below and
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implement computer vision pipelines for crop monitoring, plant stress detection, and disease identification in greenhouse environments. Apply machine learning and deep learning models (semantic segmentation
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, with research expertise in geospatial AI, deep learning foundation models, hydrology, river science. Candidates will need to have completed their Ph.D. or have it completed by the start of employment
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description As a PhD candidate, you will: - Develop and train deep-learning