389 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" scholarships in United Kingdom
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, Chemistry, Physics, Engineering, Mathematics, Computer Science, Data Science, Machine Learning or Artificial Intelligence a minimum 2:1 undergraduate degree (or equivalent) Excellent spoken and written
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Project title: Towards Greener Wars Supervisors: Dr Saeed Bagheri / Professor Benoit Mayer Project Overview: War and other military operations are responsible for a significant share of global
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This funding is available to students undertaking the following modes of study: Full-time Part-time Full-time distance learning Part-time distance learning Subject restrictions This funding is available in all
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, or other related academic discipline. Good programming skills (preferably Python). Background/work experience in Cyber Security, Machine Learning, and Finance would be highly beneficial. How to apply
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to cover living costs; Join a multidisciplinary cohort to benefit from peer-to-peer learning and transferable skills development. Learn more about the programme, available projects, and the application
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and kinematic models with machine-learning-based channel state information (CSI) prediction to enable robust, low-latency connectivity across multi-layer NTN systems. This PhD project sits
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prediction, a machine-learning surrogate model based on Gaussian process regression will be developed and trained using datasets generated by the high-fidelity numerical solver. The surrogate will emulate key
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equations to simulate pollutant transport, mixing and biochemical processes. To enable rapid prediction, a machine-learning surrogate model based on Gaussian process regression will be developed and trained
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for manufacturing operations. Process control: process modelling, control, and optimization, with applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in
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(CHF), tailored to complex geometries typical of fusion reactor cooling systems. Compile a comprehensive dataset of boiling parameters to support machine learning-based analysis of two-phase flow