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or infrastructure. This is what makes our daily work so meaningful and exciting. The Division of Computational Genomics and Systems Genetics is seeking from October 2025 a PhD Student in Deep Learning for Rare
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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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& machine learning
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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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-resolution, open-access climate projection ensembles with statistical and machine learning-based resampling techniques (e.g., k-nearest neighbours) to simulate weather-dependent energy supply and demand
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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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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