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Field
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This research project aims to establish the theoretical and algorithmic foundations of quantum adversarial machine learning, an emerging field at the intersection of quantum computing and machine learning. It
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-efficient computing Developing mathematical modeling for complex scientific problems Designing algorithms to improve the performance of scientific applications Researching digital and post-digital computer
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borehole electromagnetic data during drilling. This includes the further development and application of fast solvers for Maxwell’s equations and nonlinear inversion algorithms that we have already developed
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state-of-the-art facilities to work on the following: Developing advanced path planning, search, and exploration algorithms for multi-UAVs systems in unknown and complex 3D environments. Designing
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& Bioinformatics, is seeking candidates for a postdoctoral research fellow position. This is primarily for an NIH-funded project developing multimodal variational autoencoder models and probabilistic trajectory
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challenge meeting this requirement is the simultaneous need for low-power consumption. The main objective of the project is to develop a complete end-to-end high-performance DNN system for on-premise
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Research Fellow in AI for Healthcare (1 FTE) – ArcHub ICUSafeNotes-School of Computer Science 011287
breakthroughs into commercial healthcare solutions. In the ICUSafeNotes project, we are conducting research and development of multimodal machine learning models that includes ICU note summarisation and analysis
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advances the mathematical foundations, algorithms, and real-world applications of epistemic uncertainty in machine learning, with a strong focus on imprecise probabilities, uncertainty representation and
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on complex system subject to various constraints. Design and develop real-time algorithms. Simulate and evaluate system performance. Document research outcome to publish at international conference / journal
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems