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integrates machine learning and statistics to improve the efficiency and scalability of statistical algorithms. The project will develop innovative techniques to accelerate computational methods in uncertainty
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for greater precision. Machine learning (ML) algorithms will analyse these datasets to deliver a scalable, cost-effective system, validated through field trials and enhanced by contributions from four
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learning (ML) for high-fidelity data ‘stitching’. The integration of data from multiple analytical platforms is critical for advancing the understanding of complex biological and chemical systems. This work
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Generative machine learning models have made significant progress in recent years. Typical examples include, for example, high-quality image or video generation using diffusion models (e.g
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through the following objectives: Develop a novel approach to investigate the fluid-solid coupling effect on the performance of the CMF; Using machine-learning (deep learning) methods to develop a
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Master’s degree in a relevant discipline (cognitive neuroscience, neuroscience, computational neuroscience, psychology, cognitive science, machine learning/data science/AI). Start date: 1 October 2025
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the Novo Nordisk Foundation, that will drive research and innovations at multiple levels - from developing scalable quantum processor technologies to solutions for the quantum-classical control and readout
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, their achievements and productivity to the success of the whole institution. At the Faculty of Chemistry and Food Chemistry, the Chair of Theoretical Chemistry offers a position as Research Associate / PhD Student (m
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UC Berkeley after obtaining her PhD from the University of Amsterdam for which she did research at MIT and Sigma Computing. Her general research interest is on the intersection of machine learning
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of Amsterdam. Interested in developing fundamental machine learning techniques for tabular data to democratize insights from high-value structured data? Then this fully-funded 4-year PhD position starting Fall