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Sciences, and Mathematics. Experimental experience in fluid dynamics and/or knowledge of any CFD codes would be an advantage, but not required as full training will be given. How to apply: Candidates should
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the University’s Apply to Newcastle Portal Once registered select ‘Create a Postgraduate Application’. Use ‘Course Search’ to identify your programme of study: Search for the ‘Course Title’ using the programme code
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the 'Apply' button, above, quoting code MPB50490525. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up
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, linguistics, or data science, with experience coding in R or Python, natural language processing, and experimental design. Application Process To be considered for these PhD studentships, please complete
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. This PhD will suit candidates with backgrounds in psychology, cognitive science, linguistics, or data science, with experience coding in R or Python, natural language processing, and experimental design
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coding and CFD is advantageous but not mandatory—an eagerness to learn and innovate is key! Full training will be provided. Why This Matters Efficient storage technologies are essential for a carbon
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(including Computer Science, Physics, Maths, Engineering) Knowledge of modern machine learning techniques and experience with coding in Python is beneficial (but not a strong requirement) Applicants whose
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PhD Studentship: Open Radio Access Network (ORAN) for Distributed Edge Computing Orchestration in 6G
Language Models (LLMs). Orchestrating AI/ML pipelines in 6G. Developing certification and checking processes for code inside ORAN 6G. The research will be a combination of software engineering, radio
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of (or aptitude to learn) quantitative data analysis and coding (e.g. R). Or a background in computer or data science who can demonstrate their ecological or natural history knowledge. Candidates should have a
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/directory/egegpdfib (via the 'Apply' button above) stating course code EGEGR3 with Project: Enabling a natural capital approach to infrastructure transitions with Dr Edoardo Borgomeo. The University actively