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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
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, for model training and validation. Perform visual analysis to visualize agent behaviors, emergent strategies, and system-level outcomes. Contribute to: High-impact publications in top-tier AI conference and
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reference. Collaborating with AI scientists to provide domain insights that aid in the development of AI models. Performing experimental verifications of materials recommended by AI models. Publishing
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to apply advanced AI models in areas such as catalyst design, multi-scale modeling, and spectroscopic analysis. The Research Fellow will take on a significant role in machine learning theoretical energy
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Cryptographic Hardware Engineering System-level modelling and simulation Proven publication record in relevant peer-reviewed venues. Excellent analytical, problem-solving, and system-level thinking skills. Strong
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Hardware Engineering System-level modelling and simulation Proven publication record in relevant peer-reviewed venues. Excellent analytical, problem-solving, and system-level thinking skills. Strong verbal
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Responsibilities: Conduct programming and software development for data management. Design and implement machine learning models for optimizing data management. Conduct experiments and evaluations of the designed
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experienced Research Fellow to develop and evaluate viral vectors for CNS delivery and therapeutic expression in preclinical models. Key Responsibilities: Engineering and evaluation of vector constructs for BBB
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model for innovative medical education and a centre for transformative research. The School’s primary clinical partner is the National Healthcare Group, a leader in public healthcare recognised
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stability of local phase space for inorganic materials. Collaborate with interdisciplinary teams, including chemists, physicists, and AI/ML experts, to refine generative models with theoretical feedback