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level and across entire wind parks. Modern turbines are equipped with sensors that collect large amounts of operational and environmental data, yet translating these heterogeneous data streams
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of approaching reconstruction and variability analysis. The project combines applied mathematics, computational imaging, and structural biology. You will develop algorithms, implement and test software tools, and
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level and across entire wind parks. Modern turbines are equipped with sensors that collect large amounts of operational and environmental data, yet translating these heterogeneous data streams
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pipes in the subsurface. Innovation opportunities lie in combining physics-based knowledge with AI-assisted data analysis and data fusion techniques. By integrating multiple data sources and sensor
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lead to a PhD position at SPMS in NTU Singapore (based on performance). Key Responsibilities: Contribute to the development of algorithms Develop codebase in Python and perform large-scale simulations
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-on experience in applied robotics, sensor optimisation and product development within a collaborative SME setting. The project offers exposure to real-world storage facilities, interaction with farmers and grain
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training datasets; Design and carry out laboratory experiments to produce representative experimental training data; Develop physics-informed machine learning algorithms, trained on both numerical
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homogenisation and energy group structure. Investigate the use of AI/ML algorithms to predict or generate cross sections, enabling deterministic solvers to better capture strong heterogeneities and flux gradients
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: Develops advanced Agro-geoinformatic algorithms for monitoring and predicting the conditions of crop, pasture, and their environment with advanced remote sensing and geospatial technologies; Develops and
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to run these algorithms, i.e., the AI data centers, are extremely power hungry, thus significantly increasing the burden on the electrical grid. More importantly, the unique AI data centres load patterns