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Field
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aims at addressing computational challenges associated with data acquisition and information extraction from complex sensors and sensor networks. Crucially, uncertainty management and quantification
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project aims to address the current limitations of traditional frame-based sensors and associated processing pipelines with a new family of algorithmic architectures that mimic more closely the behaviours
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-making algorithm for autonomous vehicles. The work will involve sensor fusion, perception, trajectory prediction and test rig set-up, and experimental validation. Job Requirements: PhD Degree in Vehicle
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control laws into Trent gas turbine engines and developed algorithms monitoring fleets of 100s of engines flying all around the world. During the PhD, you will have the opportunity to deeply engage with
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operating (Waterstromen) membrane-based wastewater treatment plants. As part of the UT team, you will develop a robust model predictive control (MPC) algorithm based on sensor and other system inputs that can
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-identified scans, records and sensor feeds to answer questions such as: Can we predict a patient’s response to treatment without ever seeing their raw file? Can an algorithm learn the warning signs of trouble
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. Develop and implement control algorithms for distributed multi-agent system operations and spacecraft-based robotic manipulation. Perform research on spacecraft dynamics and control, including trajectory
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and interoperability, and efficient processing and management of time-series datasets and metadata originating from IoT sources (such as environment sensors and meters) closer to the data provider
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systems with a particular emphasis on methods and systems that cope with imperfect knowledge and uncertain sensors. The research environment provides excellent opportunities for open-minded co-operation
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component. The battery management system (BMS) unit is not just a component, but a crucial element tasked with monitoring each cell of the battery and running algorithms to calculate state of charge (SoC