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Field
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This research aims to develop an innovative framework that integrates health-seeking behaviour—the actions individuals take when they perceive a health problem, grounded in behavioural theories
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computational and experimental fluid dynamics in addition to product development. This project falls within the field of fluid dynamics, a discipline central to improving the efficiency of systems that involve
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experience with programming tools such as R or Python. An interest in acquiring new data science and programming skills is required. A keen interest in teaching and academic development. An excellent academic
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seek to attract, develop, and retain colleagues with diverse backgrounds and perspectives. We welcome applications from all genders/gender identities, Black, Asian, or Minority Ethnic backgrounds
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turbulence, and use this knowledge to identify control strategies through deep reinforcement learning. The methods developed in this project will directly contribute to designing novel porous media
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which can change over time, depending on neural activity. Incorporating the dynamics of the state-dependent delays will require the development of new tools for coupled oscillator theory in time-delayed
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geometries while delivering graded biomechanical support for key tasks like pinching, grasping, and extension. The PhD student will work on two key objectives: 1. Develop a novel method to link the geometry
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development opportunities will be provided to ensure you get the most out of your PhD experience. Furthermore, we will support you in publishing your results in academic journals and conducting public
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Partnership in collaboration with Hitachi Europe Ltd, developing engineered proteins for carbon upcycling and sustainability. The role holder will collaborate with computational and biological scientists
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changes (so called swelling). Swollen batteries are at risk of rupturing which may significantly shorten their lifetime. Development of advanced computer models is critical for understanding and