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Field
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these connected directions. Research will emphasise new learning algorithms and rigorous evaluation using public datasets, simulation and, where available, robotic and edge-computing platforms. Evaluation will
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The successful PhD candidate will undertake research in the following areas: Develop deep learning algorithms for autonomous robotic navigation using dual-view image fusion and shadow-based visual perception
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candidate will design algorithms that identify suspicious model updates, reduce the influence of compromised UAVs, and preserve useful learning from honest UAVs operating with different data and unreliable
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PhD student (f/m/d) who wants to take ownership of the software ecosystem behind our computational imaging research – from experimental reconstruction algorithms used inside our lab to robust tools used
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of probabilistic and extremal combinatorics, structural graph theory and algorithms. We study problems on discrete structures such as graphs, permutations, posets or set systems using probabilistic, structural and
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. During the PhD, you will work on topics at the intersection of probabilistic and extremal combinatorics, structural graph theory and algorithms. We study problems on discrete structures such as graphs
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control platforms, advanced microcontrollers, distributed control algorithms, and artificial intelligence techniques, including neural networks and evolutionary optimisation methods, to enable the efficient
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neuroimaging methods to better detect disrupted function following neonatal brain injury, and identify new more energy efficient learning algorithms that could reduce the economic and environmental cost of AI
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control platforms, advanced microcontrollers, distributed control algorithms, and artificial intelligence techniques, including neural networks and evolutionary optimisation methods, to enable the efficient
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pose estimation algorithms. The models and algorithms you develop will be part of open source and data repositories affiliated with the broader research program and TU Delft’s commitment to Open Science