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Field
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, audio, and sensor data. These AI models move beyond conventional predictive and purely data-driven approaches by seeking to capture the underlying causal, spatial, temporal, and semantic relationships
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Requirements We are seeking an enthusiastic and results-driven candidate who meets the following criteria: A successfully completed MSc degree in: Agricultural economics, food and agribusiness, or sustainable
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future. They represent individuals driven by curiosity and a relentless pursuit of excellence. With us, they find the space to try things out and unfold their potential. Are you inspired by their passion
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PhD Candidate to conduct research on Artificial Intelligence for managing Shipbuilding Supply Chains
methodologies, such as simulation, analytical modelling, and AI‑driven techniques, to develop decision support for efficient planning and coordination of production activities in supply chains and generate
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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learning, Efficient Algorithms and High Performance Computing), and Physics (Image Formation Modelling). Your project is part of the DUAL-IMPACT project, which focuses on the development of new data-driven
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-driven model selection, and deep learning for data analysis and feature extraction from characterisation data. Surrogate modelling will be employed to reduce computational costs, and AI-based uncertainty
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to develop a new generation of traffic prediction methods, combining traffic flow theory with machine learning, and with that, the best of both worlds: theory and logic where necessary, data-driven where
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, enabling more frequent, scalable, and data-driven asset management. The project will investigate the integration of Axle Box Acceleration (ABA), Laser Doppler Vibrometry (LDV), and Track Geometry (TG) data
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of supercomputing. However, these type of simulations have been typically driven by idealized boundary conditions that do not represent the complexity and variety of atmospheric inflows that exist in the field. PHD