381 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" PhD positions 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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-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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(Master / PhD / Postdoc). Our expertise lies in quantum foundations, quantum information theory and quantum technologies. For additional information, please visit: https://dakic.univie.ac.at/ . Your future
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emphasis on developing the payload for the ESTCube-LuNa mission. Building on previous demonstrations such as ESTCube-1, ESTCube-2, and Foresail-1p, the project aims to apply lessons learned to create a more
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funded. International applicants must demonstrate in their application how they will fund shortfall in fees by other means (personal funds,l or other grant); International applicants can visit https
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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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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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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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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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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