14 algorithm-development-"Multiple"-"Simons-Foundation"-"Prof" PhD positions at SciLifeLab in Sweden
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algorithms to detect complex structural variants in humans using long DNA sequencing reads. A structural variant (SV) is a large-scale alteration in the genome that involves rearranged, deleted, or inserted
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at the single-cell level, using tools from optimal transport, mathematical optimization, and machine learning. In addition to method development, the work includes applying and benchmarking algorithms on both
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to participate in relevant research projects and prepare for an international research career. Read more about the research at The Department of Chemistry – BMC at our website . Project description This project
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are always looking for curious minds that see themselves imagining the unimaginable with us. United As One for Patients, our purpose in Healthcare is to help create, improve and prolong lives. We develop
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will use machine learning methods to develop affinity ligands. These methods have been transformative for protein design, allowing generation of novel proteins which can suit a precise need. In this 4
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Data-Driven Life Science (DDLS) aims to recruit and train the next generation of data-driven life scientists and to create globally leading computational and data science capabilities in Sweden
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ecosystems. The SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) aims to recruit and train the next generation of data-driven life scientists and to create globally leading
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The section of Radiology at the Department of Surgical Sciences, Uppsala University, is organized in several research groups that conduct research within the development and application of imaging
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and Wallenberg National Program for Data-Driven Life Science (DDLS) aims to recruit and train the next generation of data-driven life scientists and to create globally leading computational and data
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. The student will work in a group addressing all these challenges, developing new AI-based methods to improve biological realism in simulations which will lead to more accurately inferred GRNs from real data