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define observable events based on expert knowledge and available evidence. Development of a post-race analysis structure, process and data ‘toolkit’ that can build on historical understanding of race
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, engineering, physical sciences, economics or any other field where evidence is provided of sufficient computing and mathematical background) solid experience of programming, machine learning methods and ideally
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the ‘Research Area’ blank Select ‘PhD in Process Industries; Net Zero (PINZ)' as the programme of study You will then need to provide the following information in the ‘Further Details’ section: A
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: Search for the ‘Course Title’ using the programme code: 8856F Leave the ‘Research Area’ blank Select ‘PhD in Process Industries; Net Zero (PINZ)' as the programme of study You will then need to provide
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, Physical Sciences, Computer Science & Maths): These are fellowships for women scientist and engineers. The candidates must be from eligible low and middle-income countries (find the complete list here: https
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overall, 6 in each category). Acceptable first degree: Computer Science/Physics/Maths or other numerate discipline. Mode of Study Full-time Start Date 1 October 2026 Funding Information ARIES studentships
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a first or upper second (2.1) class Master's degree (or equivalent) in chemistry, physics, materials science, computer science or other related discipline. Candidates with strong BSc (Hons) degrees in
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. Development of novel processing techniques Modelling techniques that can inform the direction of experimental activity Physical, mechanical and materials characterisation techniques Data-driven approaches
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PhD in Quantum Error Correction and its Applications School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr Yingkai Ouyang, Dr P Kok Application Deadline: 15 January 2026
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observations and modelling of the physics and biogeochemistry of Antarctic shelf seas. You will gain experience in computer coding, statistics for environmental science, working with and piloting autonomous