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of the largest pan-cancer signaling models in the literature. SPARCED is compatible with high-performance and cloud computing, can simulate thousands to millions of single-cell trajectories, is easily expandable
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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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analysis of such data will thus be a core activity in the doctoral program. Furthermore, performing flow cytometry analyses and cell sorting will be central and also immunological assays will be used
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the Knut and Alice Wallenberg (KAW) Foundation. The DDLS program funds 50 high-profile young group leaders (“Fellows”), over 210 postdoctoral positions and has established a research school for 260 PhDs
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stage. Strong programming skills (Python or R) and familiarity with high-performance computing Exceptional collaborative abilities Preferred qualifications A doctoral degree or an equivalent foreign
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of image analysis pipelines on local and national high-performance computing resources in collaboration with NBIS, the SciLifeLab Data Centre, and DDLS fellows. Contribute to data stewardship by supporting