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concepts underlying the GIG into efficient data structures and algorithms. Your work will also involve developing algorithms to manipulate and analyse GIGs, creating efficient methods for translating GIGs
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to the activities of the Machine Learning & Data Science research group, develop rigorous mathematical proofs for convergence and asymptotic analysis of deep learning models, methods and algorithms, and collaborate
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advance the fundamental science of artificial intelligence and address some of the field's most important challenges. You will be responsible for researching and developing novel algorithms and techniques
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Professor Justin Sirignano at Oxford and Professor Konstantinos Spiliopoulos at Boston University. The project develops new mathematical theory for training algorithms and neural network models, drawing
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Location: South Kensington (Hybrid) About the role: We are looking for a Postdoctoral Research Associate to join an Airbus- and Aerospace Technology Institute (ATI)-funded project to develop data
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to develop and implement state estimation algorithms, such as Kalman filters and observers, for real-time applications. Proficiency in MATLAB and Simulink for modelling, simulation, and validation using
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demonstrations. The successful candidate will work at the intersection of multi-disciplinary modelling, advanced AI algorithms, and decision-support tool development. Responsibilities will include programming
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persist across participants, and retraining from scratch is often computationally infeasible at scale. In this project, you will develop the next generation of federated machine unlearning algorithms
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both to develop algorithms for future fault-tolerant devices and to conduct tests on real quantum hardware. Across the project, the student will identify the main bottlenecks in speed, accuracy, and
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. The role spans the entire research pipeline – from processing spatio-spectral-temporal UAV imagery to model development, algorithm validation and deployment, including contributions to a QGIS forest