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science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular processes to human health and
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, calibration and reliability of large pre-trained models Probabilistic generative models and world models Probabilistic machine learning for scientific discovery Don’t see your exact idea listed? We encourage
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conformational transitions induced by ligand binding, cofactors, metabolites, stress, or post-translational modifications. While recent deep-learning methods such as AlphaFold have transformed protein structure
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, from molecular structures and cellular processes to human health and global ecosystems. The SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) aims to recruit and train the
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. •Contribution to instrument maintenance, troubleshooting, SOPs and quality documentation. •General laboratory activities required for reliable and efficient operation of the infrastructure. •Close collaboration
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, thoroughness, a structured approach to problem-solving, and the ability to work both independently and as part of an interdisciplinary team. Additional qualifications Experience in simulation-based or likelihood
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these analysis methods to highly informative multimodal microscopy data and develop techniques to integrate correlated structural and molecular analysis into the natural 3D tissue space. This integration will
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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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bias, including previously detected structural variation on sex chromosomes, and analyze the evolutionary history of these regions within and across species. Overall, this project will provide novel
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biology and bioinformatics, microbiology and immunology, molecular biology, molecular biophysics, molecular evolution, molecular systems biology, and structural biology. While the foundation of our research