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: Transparency. As part of this doctoral project, you will develop novel knowledge representation techniques, algorithms for human-in-the-loop optimization, and interactive tools that combine graphical and natural
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research community. investigating whether observed evolutionary tendencies are universal or language specific. contributing to the intersection of computational linguistics and the Social and Behavioural
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worldwide. Your work will include: Apply existing machine learning algorithms for automated detection of CO plumes in satellite observations Building a global catalogue of CO emission events from 2018 onwards
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learning paradigms. The framework will support rapid prototyping, automated design-space exploration, and cross-technology benchmarking, providing new insights into the co-design of learning algorithms
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and educational outcomes. Responsible AI and fairness auditing. Conduct algorithmic fairness validation of the CLARA system, develop documentation on data governance and GDPR compliance, and contribute
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use, and long-term crop management goals. While several algorithms have shown promising results in energy savings and crop yield, most of these methods have only been tested in simulation, and make use
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sovereignty, and cyber-electromagnetic resilience. The PhD researcher will primarily work within Tilburg University’s AI research infrastructure, focusing on algorithm development, model training, and
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scheduling algorithms for fast control and reconfiguration of the optical AI compute clusters. Realize a small-scale compute cluster lab testbed to demonstrate and evaluate the performance of the innovative
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of service. The project is conducted in collaboration with two clusters at the Department of Mathematics and Computer Science of TU/e: Data and Artificial intelligence . Novel learning algorithms will
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, retrain the stimulation-control algorithm for stroke-specific gait, and evaluate usability, comfort, and fit during prolonged use. You will also initiate adaptable, IMU-based stimulation control