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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 development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
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-performance computing. SLU provides access to extensive datasets that can be used to develop machine learning methods and automated analyses relevant to the position. Long-term datasets are available from, i.a
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guided by supervisors. Doctoral studies end with a thesis and a doctoral degree. More about being a doctoral student at LTH on lth.se. https://www.lth.se/english/study-at-lth/phd-studies/ Subject and
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experimentally challenging and we are using specially-developed methods for library prep for high-throughput sequencing to achieve this. The research involves next-generation sequence library preparation in
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the department’s activities within method development for machine learning-based computational biology. The duties include supervision of PhD students and postdocs, and teaching at basic, advanced and research level
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Data Driven Life Science (DDLS). About the DDLS Fellows program Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes
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and developing the unit’s analysis tools, data transfer and data/metadata management, and deposition of DNA data/metadata in repositories. Requirements We are looking a person with a PhD in
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promising properties. The project could be combined with experimental work if this is a fit for the applicant. Your profile To be eligible for employment as a postdoctoral researcher, a PhD or a foreign
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with nano-LCs. Performing data analysis and interpretation using established proteomics software and tools, such as Proteome Discoverer, Spectronaut, Skyline, Byonic, Perseus and R-based Shiny