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to retrieve geophysical information from satellite data. Our research drives innovation in instrumentation and retrieval algorithms, and tackle climate change, air pollution, natural hazards, and land/ocean
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transparent models. The Privacy-aware transparency decisions research group (led by Prof. Vicenç Torra) conducts research in data privacy for data to be used for machine and statistical learning. It is well
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modular, scalable, and transparent control algorithms suitable for real-time implementation across different vehicle platforms. - Contribute to theoretical developments in stochastic model predictive
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includes signal processing with emphasis on development and optimization of algorithms for processing single and multi-dimensional signals that are closely related to applications and applied research
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. The overall aim of this project is to address these challenges by: Developing new data-driven and physics-based models of battery behaviour. Designing advanced BMS algorithms for real-time monitoring and
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system complexity. Your work will include: Developing modular, efficient, and transparent control algorithms. Combining model predictive control with learning-based motion prediction under uncertainty
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well as topics in phylogenetics. This project will involve working closely with experimentalists, and will be co-supervised by Prof Gerald McInerney and Dr Daniel Sheward, who have expertise in virology and
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the developmental rules underlying phenotypic variation. The successful postdoctoral fellow will develop and implement an empirical framework that utilizes data-driven algorithms to learn relationships between past
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passive and active flow control algorithms, potentially incorporating machine learning/AI, to enhance aerodynamic performance and stall delay with rapid response times. The research is conducted in
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-fidelity qubits operations Design and implementantion of automatic calibration techniques for fast tune-up Implementation and benchmarking of quantum algorithms About you You have a relevant PhD deegree