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an outstanding and ambitious postdoctoral researcher in computational biology to pioneer understanding and modeling of tissue architecture using single-cell and spatial transcriptomics data. The focus will be
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research group funded by the Data-driven Life Science Fellows (DDLS) program. Led by Wei Ouyang, the group builds next-generation AI systems for cell and molecular biology. Data-driven Life Science Fellows
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interpretable models that connect molecular patterns across multiple scales of human biology. The project lies at the interface of artificial intelligence, computational biology, systems medicine, and precision
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Research (CGR) Lab , within the Data Science and AI division, Gothenburg, Sweden. About us The Department of Computer Science and Engineering , a joint department of Chalmers and the University of Gothenburg
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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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computer science, image analysis and machine learning, engineering physics, data science, applied mathematics, molecular biotechnology engineering, or another related field; or Have completed at least 240
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composed of the following four departments: The Department of Infectious Diseases The Department of Microbiology and Immunology The Department of Medical Biochemistry and Cell Biology The Department
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School of Engineering Sciences in Chemistry, Biotechnology and Health at KTH Job description Quantitative analysis of lipid nanoparticles (LNPs) using Cryo EM is challenging due to heterogenous
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of pancreatic cancer. This group is led by Associate Senior Lecturer Dr. Qiaoli Wang, as part of the SciLifeLab & Wallenberg National Program for Data-Driven Life Science (DDLS) . Group members are enrolled in
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Science (DDLS) is a 12-year initiative that focuses on data-driven research, to train and recruit the next generation of life scientists and create strong and globally competitive computational and data