31 structures "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" positions at SciLifeLab
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artificial intelligence applied to large-scale molecular data are transforming the study of biological systems at all levels, from molecular structures and cellular processes to human health and global
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, volumetric analysis, and modeling of structural heterogeneity in biological macromolecules. Rather than only applying established workflows, you will explore new computational formulations and alternative ways
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to contribute to applications for external research funds. In this project, your responsibilities will include leading the investigation of structural and biophysical properties of miniaturized tumor environment
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at all levels, from molecular structures and cellular processes to human health and global ecosystems. The SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS), coordinated by
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services can be found at: https://www.uu.se/forskning/snpseq and https://ngisweden.scilifelab.se/ We are proud to deliver high-quality data and are accredited by SWEDAC as a testing laboratory under the ISO
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Collaborative abilities Independence Well-organized / structured Have a professional approach Analyze and work with complex issues Good knowledge of English and good presentation skills in text as it is required
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identity and extracellular signaling, but how cell types, signaling mechanisms, and transcription factors jointly determine tissue structure is incompletely understood. The Koplev lab is recruiting
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. Read more about our benefits and what it is like to work at Uppsala University https://uu.se/om-uu/jobba-hos-oss/ The position may be subject to security vetting. If security vetting is conducted
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protein analysis methods Preferred qualifications Experience from biotech/pharmaceutical industry or relevant academic research environment Experience with structural biology, especially cryo-EM Experience
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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