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—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
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drone detection and localisation performance using radar systems. This can be achieved by improving the detection performance of individual sensors and by employing a cooperative network of sensors which
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sustainability. The research will delve into power-aware computing strategies, thermal management, and the development of algorithms that balance performance with energy consumption. Students will aim to create
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sensor signals into reliable state information and use this information to enable real-time, closed-loop operation. A key strength of these projects is that your work will not remain “simulation-only.” You
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leading aerospace organizations. It is a subproject of a NATO-wide initiative where participating organizations can test new control algorithms on sub-scale 3D-printed aircraft, developed and provided by
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series data. Large data sets come with significant computational challenges. Tremendous algorithmic progress has been made in machine learning and related areas, but application to dynamic systems is
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available in Digital Endocrinology in the Marie Skłodowska-Curie Doctoral Network (ENDOTRAIN). Join Europe’s first doctoral network in digital endocrinology – integrating AI, sensor technology, omics, and
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Allowance** EUR 4736 EUR 710 EUR 660 Context: Agriculture and agronomy generate a wide variety of data (connected equipment, weather, environmental sensors
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technologies such as IoT, big data, analytics, computer vision, cloud computing, and artificial intelligence (AI). IoT devices help in data collection. Sensors plugged in tractors and trucks as well as in fields
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twins, enabling real-time stress-testing under simulated edge cases like cyber-physical attacks and sensor failures. The Research Challenges There exists a complex interplay of factors that present