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developing the theoretical and algorithmic foundations of compositional world models. A key application focus of the grant lies in rapid and safe real-world skill acquisition in application domains such as
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develop throughput-optimal entanglement distribution algorithms (both centralized and decentralized algorithms) for quantum networks with resource constraints. The project is funded by the EPSRC AI hub
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develop throughput-optimal entanglement distribution algorithms (both centralized and decentralized algorithms) for quantum networks with resource constraints. The project is funded by the EPSRC AI hub
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Prof. Neil Walton (Durham University, UK). The general aim of this project is to develop throughput-optimal entanglement distribution algorithms (both centralized and decentralized algorithms
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staff position within a Research Infrastructure? No Offer Description The selected candidate will develop innovative and efficient resource optimization algorithms, suitable for this context, with cost
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experimental evaluation of algorithms. Prepare and publish research findings in peer-reviewed journals and present results at conferences. Assist in the mentorship of graduate and undergraduate students
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and image analysis (CT/MRI) using computational approaches. The postdoctoral fellow will use machine learning and develop algorithms to delineate the crosstalk between different cellular compartments
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, understand the design and development decisions that propagate social biases, and develop theoretical and algorithmic approaches to mitigate them. Key responsibilities include developing bias detection tools
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mathematics. Develop and implement machine learning algorithms, with a focus on reinforcement learning, to solve challenging mathematical problems. Collaborate with other team members to advance the research
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to develop complement/augment classical CFD methods with quantum algorithms/techniques. The work lies at the intersection of multiphase flow physics, numerical modeling, and quantum computing. Who we