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Field
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experts in smart cities, artificial intelligence, transportation planning, and urban sustainability. We are seeking applicants interested in developing an efficient system’s approach for optimising urban
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data Development of algorithms for infection and evaluation of infection hotspots in the plant population Coordination of the scientific interface to the project partners with regard to entomological
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for behavioural and security properties; efficient algorithms for model checking, learning and synthesis; improved explainability and safety of machine learning models, e.g. by integrating neural and symbolic
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is to develop a highly innovative ‘Lab on a Bench (LoB)’ setup, integrated with Machine Learning algorithm, as a high throughput method for screening and developing formulated products that are used in
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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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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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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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-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