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conducted to assess the feasibility of prototypes in terms of activity tracking, updating game elements, and visualisation capabilities. The second objective of the thesis is to develop models and algorithms
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of hardware and control algorithms for real-world applications. The Electrical Power Group is expanding rapidly with ambitious plans. We work closely with leading industry partners across multiple sectors and
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and training. -Support processing and integration of datasets, including UAV imagery, satellite data, IoT sensor data, and field observations. -Contribute to workflows for AI/ML applications, crop
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on real-life data from the maritime industry. As part of the PhD, you will be following advanced courses to extend your skills, implement and test algorithms, and learn to write scientific papers. As part
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algorithms for resource-efficient learning, for example via data selection and filtering (leveraging that not all data is equally informative). You will also investigate complementary approaches that reduce
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/m/d) Development of soft-sensors connected to particlebased separation models to control flotation processes. Your tasks Develop and implement soft‑sensor concepts for continuous monitoring of ore
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address domain-specific challenges in biomedical applications. The PhD project will focus on translating and enhancing cutting-edge algorithms from AI research into concrete applications in biomedical
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composites toward a new generation of smart structures with built-in sensing and intelligence. By integrating optical fibre sensors directly into composite materials and combining them with physics-based
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in a team environment Highly organized with excellent communication and interpersonal skills Preferred Qualifications: Experience with eye-tracking or wearable sensor technology Background in clinical
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the Unconventional Communications and Computing Laboratory (UC2), led by Dr Michael T. Barros, which develops modelling and algorithmic methods for networked communication and computation under real-world constraints