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bioinformatic activities, focused on developing and applying Multi-Objective Optimization (MOO) algorithms and Pareto-front thinking to understand how different individuals/lineages come to possess different
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healthcare, in most cases, there is only access to information at the patient level, about the patient’s health status and disease development. In this project, we will develop theory, algorithms and methods
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(Spatial VDJ). Using established and newly developed algorithms, we map B cell evolution within tissues, including class switching and somatic hypermutation, and identify putative candidate tumor-regulatory
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and perspectives lay the foundation for learning, creativity and development. We welcome people with different backgrounds and experiences to apply for the current employment.
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of approaching reconstruction and variability analysis. The project combines applied mathematics, computational imaging, and structural biology. You will develop algorithms, implement and test software tools, and
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evolution across different genomic regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods and statistical analysis (https://cgrlab.github.io
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on methodological development in cryo-electron microscopy (cryo-EM), particularly in image reconstruction and 3D volumetric analysis of macromolecular structures. Rather than aiming to incrementally optimize existing
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to human health and global ecosystems. The SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS), coordinated by SciLifeLab, aims to recruit and train the next-generation of data