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challenging properties of uncertainty, irregularity and mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and
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mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and machine learning frameworks such as recurrent
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to office, lab space, and specialist software Opportunities to attend international conferences (e.g., USA) Involvement in Cranfield’s Doctoral Network, providing training and networking across disciplines
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for translating highly sensitive quantum technologies out of the lab and into the field, enabling applications ranging from underground mapping and navigation to gravity-based surveying and space-based sensing. You
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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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to work with international partners such as the WHO. The models developed will used to answer a series of substantial questions related to variation in exposure to air pollution over space and time
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within the University. The activity spans across land, off-shore, marine, air and space power and propulsion applications, with a particular specialisation in gas turbine engineering. This exciting project
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ways in which existing demand profiles for heat (space and water) is characterised by the technology of delivery; • To develop a catalogue of synthetic demand profiles for ASHPs and heat networks
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doctoral training programme dedicated to academic research in space propulsion. R2T2 PhD programmes are already underway at nine UK universities, and the programme overall is centred on the Westcott facility
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. Given equal qualifications, preference will be given to female candidates. University of Vienna. Space for personalities. Since 1365. Data protection Application deadline: 09/02/2025 Prae Doc