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Field
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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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stability. Affinity with numerical simulation, algorithm development and scientific programming. Excellent communication skills in English. The following are considered assets: Knowledge of power electronics
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Communication sciences Information science Engineering » Communication engineering Engineering » Electronic engineering Mathematics » Applied mathematics Mathematics » Probability theory Mathematics » Algorithms
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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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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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de facto algorithmic management, and when employees perceive leadership as no longer meaningfully human-led. The second stream focuses on leaders themselves. It explores how increasing reliance on AI
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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
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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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algorithms for analyzing single-cell (multi)-omics data and design and perform in silico experiments to test them; be an active and responsible member of the research group and collaborate closely with fellow