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Start Date: 1th October 2026 Application deadline: 26th March 2026 Introduction: This PhD project in Thermo-structural design of hypersonic vehicles under extreme material nonlinearity will be
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recommend that you apply early as the advert may be removed before the deadline. This fully funded PhD position, offers an exciting opportunity to develop and optimize multiscale models for surfactant
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About the Project An exciting PhD project on the effects of heat transfer of transitional compressible boundary layers will be carried out under the UK Hypersonics Doctoral Network, which has been supported by the Ministry of Defence and EPSRC for building the necessary expertise to develop...
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scenario. Objectives - Create forward models of the wave-interaction and crack propagation processes. - Use inversion methods to extract defect information from the scattering data. - Develop a probabilistic
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About the Project An exciting PhD project on the effects of heat transfer of transitional compressible boundary layers will be carried out under the UK Hypersonics Doctoral Network, which has been supported by the Ministry of Defence and EPSRC for building the necessary expertise to develop...
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: Establish a tailored enzyme panel for site-selective late-stage functionalization of complex NP scaffolds. Optimize biocatalytic reactions to generate a focused library of new NP analogues. Evaluate
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pharmaceutical sectors, with the aim of real deployment, not just academic output. The work will involve: Understanding how the material behaves during processing Determining operating conditions that meet
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the noise associated with near-term quantum devices. This in turn offers an exciting new dataset from which it will be possible to use machine learning to train a more accurate functional for use in density
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Scheduled Hours 37.5 Position Summary The Projects Specialist for WashU’s Office of Public Scholarship is responsible for supporting creative projects, collaborative programming, and strategic
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the noise associated with near-term quantum devices. This in turn offers an exciting new dataset from which it will be possible to use machine learning to train a more accurate functional for use in density