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Monte Carlo simulated data, but there is also the possibility of working with real open data from the ATLAS and/or CMS experiments. The methods are general and applicable well beyond particle physics
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candidate will join the Scientific Machine Learning group at TDB and SciLifeLab. The group develops theory, methods and software for data-driven science, with a current focus on uncertainty quantification
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and variation. The successful candidate will develop innovative methods and models to advance our understanding of genome evolution and variation. The position is based in the Computational Genomics
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single-cell methods in technically challenging plant systems represents a central challenge in modern plant genomics. While single-cell and single-nucleus sequencing approaches have transformed research in
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of the position is method development. We are seeking a postdoctoral researcher to develop next-generation AI models of cellular dynamics, with a focus on scalable neural network architectures that represent
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on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
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methods are developed in parallel, the postdoc will develop systems and services that make biological data accessible to AI and computational tools, collaborating closely with the Human Protein Atlas (HPA