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Field
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PhD at the Forefront of Computational Solid Mechanics and Machine Learning School of Mechanical, Aerospace and Civil Engineering PhD Research Project Self Funded Dr J L Curiel Sosa Application
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machine learning pipeline to derive governing equations and boundary conditions for macroscopic crowd models from synthetic and real-world data. Close collaboration with a dedicated PhD student, who is
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Butts’machine learning sub-group. Supporting the development of grant funding applications around machine learning for structure elucidation. You hold a PhD (or are in final stages of preparing to submit for a
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Educational NLP with Multi-Agent and Multimodal LLMs for Inclusive and Collaborative Learning School of Computer Science PhD Research Project Directly Funded Students Worldwide Dr Zheng Yuan
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(Python, C++). - Knowledge of AI, machine learning, control systems, or reinforcement learning. - Ability to work independently, communicate effectively, and contribute to collaborative research. Desirable
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massive substrate profile datasets in high throughput; application of in-house machine learning algorithms to identify optimal linker activity and selectivity; synthesis and characterisation of linker
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, Organometallic, Organic Chemistry and Machine Learning for a period of up to 36 months. The project, funded by EPSRC, will involve exploring the use of machine learning to develop new tools for investigating
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for: A PhD in High Performance Computing, Machine Learning, Computer Science, Applied Mathematics, Data Science, or equivalent research, industrial or commercial experience Practical experience with
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testing. They will hold a PhD in the field of machine learning and prediction/decision modelling or related discipline. Applicants are expected to hold or achieve Advance HE Associate Fellowship
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: Essential criteria PhD qualified in Mathematics, Physics, Engineering or Computer science or near completion* Experience in language computer programming (Python) General experience in machine learning/deep