209 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:" PhD positions in United Kingdom
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, alongside their application to challenging chemical problems. The group combines pulse sequence design, experimental NMR, and computational approaches, including modelling, AI, and machine learning
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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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series analyses, (2) Earth or planetary remote sensing, (3) Data science approaches, including statistical methods, handling of large datasets, pipeline development and/or machine learning (4) Full stack
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applicants who have a background or strong interest in Computer Science, interactive media, software engineering, 3D modelling/animation, VR/AR, human–computer interaction or related digital-tech fields
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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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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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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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(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