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and so on is often cost and space prohibitive. Here therefore, the focus is on methods to enable robotic platforms to perform certain tasks in collaborative mode, such that they can be shifted from task
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. The project will contribute to research within human–computer interaction and human factors on topics including: human–AI teaming in safety-critical work AI transparency and explain-ability in operational
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on the analysis of complex event history data, with some relevant topics being the analysis of outcomes under competing risks, studies of individual heterogeneity, estimation using machine learning methods and
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plant research and methods including growth analysis and gas exchange measurements Knowledge and/or interest in whole plant physiology (from soil to plant) Good computational skills and a working
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the development of methods or frameworks to support secure and trusted data sharing for maritime AI model development. Depending on the candidate’s background and the project’s progression, the research
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to an expected tipping point in robotics, where robots graduate from mere tools to teammates. The PhD candidate is expected to apply methods in human-centred design (HCD) to one or more real cases of embodied
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, employing multivariate analysis (MVA) and advanced data-driven approaches, including artificial intelligence (AI) methods. The material mapping results will be validated using complementary analytical
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animal trials Comprehensive understanding of modern dairy production Documented experience with data handling and statistical methods Documented experience with animal behavior analyses or laboratory
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circular, nature-based method for removing PFAS from soil, water, and food systems. We combine three innovative steps: Phytoremediation: Plants absorb soluble PFAS from contaminated soil. Pyrolysis
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lifecycle. Adaptive learning systems evolve through continuous updating, which introduces new challenges in reliability and assurance. Traditional engineering methods assume relatively static system behavior