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university research into commercial outcomes. Under this program, PhD students will gain unique skills to focus on impact-driven research. This Project aims to develop a predictive machine learning model
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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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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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, 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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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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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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Biological Dark Matter (BDM). A main idea of this project is to make use of the pattern matching abilities of the Tsetlin Machine in machine learning to be able to recognize signals in the BDM in
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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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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