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Field
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learning and experience in two or more of: computer vision, sensors/sensor fusion, robotics fundamentals. • Proficient in programming languages such as Python and C++; experience with frameworks such as
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sensors that can be independently transmitting, receiving, or both. By acting in unison, rather than in isolation, they can utilise temporal and spatial diversity whilst simultaneously exploiting shared
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management of microgrids, battery scheduling, and handling uncertain renewable generation without relying on forecasted data [1]-[5]. Studies reveal that parametric tuning of RL algorithms such as state and
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drugs at the target site, using only an internal battery and on-board sensors for fully autonomous operation. The overarching goal is to develop a battery-powered ingestible capsule that autonomously
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project’s focus is to: Conduct cutting-edge experiments to investigate how surface texture affects seal performance and explore the use of an ultrasonic sensor for real-time monitoring. Experiment with
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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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will develop autonomous on-board guidance algorithms for space missions using open-source numerical solvers for convex optimisation developed at the University of Oxford. The focus will be on designing
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microclimates that demand dense sensor networks and reliable data retrieval. This project focuses on developing nature-inspired hardware to deploy Internet of Things (IoT) sensors in forest ecosystems. Combining
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• Experience in bio-instrumentation design, specifically in integrating biological or chemical assays with electronic sensors • Experience with NFC technology • Experience developing mobile applications
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interpreted by regression and tree-based machine learning algorithms to obtain even better mutants and develop mechanistic hypotheses. Various collaborations with ON-TRACT network partners across Europe allow a