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networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our
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of predictive models for energy demand and production. These models will leverage techniques such as time series analysis and machine learning and will be integrated into a digital twin platform. The aim is to
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, criterion handling and machine learning. Topic The main research objective is to contribute to the development of responsible AI, with a strong focus on trust and confidence handling when dealing with data
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are looking for a highly motivated and skilled PhD researcher to work on graph-based machine learning surrogates of wind energy systems. Our goal is to accelerate flexible fatigue load estimation
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regulatory network reconstruction and wide range of machine learning approaches The host labs will provide financial support for the whole length of the PhD. The applicant will be expected to seek independent
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-type specific samples, state-of-the-art molecular biology techniques, multimodal data generation and integration, gene regulatory network reconstruction and wide range of machine learning approaches
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to the development of neuroadaptive learning technologies grounded in real-time human feedback. Job profile We are looking for a highly motivated PhD candidate with a recent Master's degree in engineering and a strong
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, Ghent University is a logical choice for its staff and students. Doctoral fellow A 4-year PhD fellowship is available in Precision Soil and Crop Engineering (Precision Scoring - https://www.ugent.be/bw
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and near infrared, mid infrared and advanced machine learning and artificial intelligent modelling to enable accurate monitoring of nitrogen mineralization rate to enable understanding and improving
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Job description Short job description We are seeking a highly motivated and talented PhD researcher in the field of system identification and control engineering for mechanical/mechatronic systems