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networks, making use of intelligent AI-driven control planes. The work will involve: i) (i) development of network probing algorithms for OSNR/GSNR measurement automation; (ii) development of adaptive ML and
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. Examples of topics include algorithmic fairness in network analysis, developing network embedding frameworks for real-world network datasets or AI models based on agentic LLMs for simulating real-world
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the theory of optimization algorithms and high-dimensional statistics to address some of the most fundamental questions in ML such as the behavior of neural networks. The environment of this project is highly
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algorithms to detect complex structural variants in humans using long DNA sequencing reads. A structural variant (SV) is a large-scale alteration in the genome that involves rearranged, deleted, or inserted
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. Candidates must have a strong motivation for research and excellent programming skills. Expertise of developing computer vision and machine learning algorithms would be desirable, with an interest in image
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-based and index-based approaches, the sequent-peak algorithm, extreme value analysis, and multivariate copulas. Based on this, you will develop an improved method to map global energy drought risk and
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well as the performance of active flight control systems remain underexplored. Addressing these gaps is essential for the success of this technology. This research project aims to support the development of safer, more
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, electronic, robotic and software engineering skills to develop and evaluate machine vision-based algorithms for automated plant care in a controlled environment with artificial lighting. Relevant tasks will
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of infection, immunology, and adaptation of immunity across life. We aim to recruit a bioinformatician to complement our expertise and to develop an individual research focus within our research team. We offer
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This research opportunity invites self-funded PhD candidates to develop advanced deblurring techniques for retinal images using deep learning and variational methods. Retinal images often suffer