374 machine-learning "https:" "https:" "https:" "https:" "https:" "The Francis Crick Institute" PhD positions in United Kingdom
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applications from all sections of society. How to submit your application Please complete the online application form via https://qpl.edgehill.ac.uk/apex_qlive/f?p=QL4S:LOGIN:::NO:SESSION:APP_SERVICE
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-making autonomy and human-machine teaming. The use of logic and data to make decisions, solve problems, and learn. Moving from rule-based systems to agents with strategic flexibility. The range and
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information from the Doctoral College website: https://www.herts.ac.uk/research/research-degrees-and-doctoral-college Interviews will take place: April 2026 (exact dates TBC) For informal enquires please email
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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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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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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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expertise in programming (C++, Python), computer architectures and Deep Learning (PyTorch, TensorFlow). Exceptional international candidates may be eligible for a fee waiver (read the following section
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network integration for emerging low-energy opto-electronic AI systems and beyond. The challenge: Machine learning and neural networks are super-charging the complexity of problems that computer algorithms