359 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" PhD scholarships in United Kingdom
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, preferably at Masters level (in exceptional circumstances a 2:1 degree can be considered). To apply visit: http://www.nottingham.ac.uk/pgstudy/apply/apply-online.aspx For any enquiries about the project please
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simulation results with experimental data. This project will integrate advanced AI techniques, including machine learning for parameter optimisation (e.g., Bayesian optimisation, reinforcement learning), AI
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. This PhD project aims to create advanced XCT workflows by developing Artificial Intelligence (AI) and Machine Learning (ML) tools to support imaging before the reconstruction phase. The research will focus
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, and space hardware. This PhD research aims to develop a comprehensive Mode Selection Framework for Reduced Order Modelling (ROM) in Structural Dynamics—using machine learning to build robust
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with NEOM, one of the world’s largest ecological restoration programmes, the project will develop machine-learning approaches to analyse satellite observations of vegetation change and evaluate large
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harvesting recent breakthroughs in Machine Learning (ML) and analytical modelling. Specifically, this project seeks to quantify key performance metrics and create powerful adaptive ML-driven management methods
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framework integrating physics-informed machine learning, scenario generation, and human-in-the-loop preference-based reinforcement learning to prioritise climate-robust and equity-aligned interventions
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programming (e.g., Python/C++), machine learning frameworks, or robotics software environments such as ROS. You are motivated to work in a multi-disciplinary research environment combining engineering, AI, and
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training programme at the start of the PhD to develop skills in areas such as programming, data analysis, machine learning and signal processing. This will provide the technical foundation required to work
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modelling and oomph-lib for continuum mechanics simulations, enabling the integration of discrete and finite element methods. Coupled with machine learning techniques, this approach will address the complex