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Field
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modeling & environmental risk assessment. Numerical simulation techniques for hydrogeological systems. Advanced uncertainty quantification for robust modeling. Scientific communication, including
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countries and beyond. Training and Career Development As a PhD student, you will join the Faculty of Science Graduate School, which provides a comprehensive programme of professional skills training tailored
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quantification and data science. Potential investigation areas: • Enhancing Monte Carlo and Markov Chain Monte Carlo (MCMC) with reinforcement learning. • Developing adaptive tuning and continual learning
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Engineering, Mechanical Engineering, Aeronautical Engineering, Automotive Engineering or other relevant Engineering and Science subjects, or relevant industrial experience. English language requirements
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tunnel (IWT) based at Cranfield which has a strong collaborative history with industry in the field of atmospheric icing science research. This programme provides the PhD candidate with an outstanding
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degree or a master’s (or international equivalent) in a relevant science, mathematics or engineering related discipline. Excellence in computational science and mathematics Programming skills in any
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degree or MSc degree (Distinction/Merit) in engineering, computer science, or other closely related fields. Experience with ROS and proficient programming language skills (C++, Python, MATLAB) would be
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at the interface between stochastic modelling, signal processing and data science. Ultimately, the project will develop key indices that can be used to assess the health of the soil ecosystem. Such indices
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research programme funded by the Academy of Medical Sciences Springboard award. This project aims to explore the role of these neighbouring glycoproteins in neurotrophin-mediated neuronal development as
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at Cranfield which has a strong collaborative history with industry in the field of atmospheric icing science research. This programme provides the PhD candidate with an outstanding opportunity to work across a