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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
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systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization
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of algorithms, machine learning, optimization, scientific software development and high-performance computing. The division is also an important part of the eSSENCE strategic collaboration on e-science and of
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algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics, spanning diverse application domains such as medicine, energy systems, biomedical
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employing a combination of genetics, molecular biology, including Ribo-Seq, RNA pull-down and mass spectrometry, reporter assays in cells and tissues, and computational sequence analyses. This implies to work
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, the interplay between genetic variation and human phenotypes, and the development of AI-driven approaches to better understand human biology and disease and to translate these insights into data-driven strategies
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the genetic material, and how it interacts with various kinds of micro-organisms. Using this knowledge, we try to elucidate the causes of diseases, and find new ways to diagnose and treat them. The Institute is
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Uppsala University, Department of Ecology and Genetics PhD position in evolutionary genomics Would you like to conduct research on the evolution of sex chromosomes and sex ratio, supported by
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computational methods with a particular focus on deep learning and image analysis. The project relies on a close collaboration with researchers at the Department of Immunology, Genetics and Pathology (IGP