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human health. Within this mission, the Jug Group develops advanced computational methods and open scientific software to extract knowledge from complex biological imaging data. We work at the intersection
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QGG - Aarhus University seeks a postdoc researcher in sustainable breeding: developing simulation...
and scientific exchange. A high degree of professional engagement and involvement in study designs, data analysis, and method development - your ideas and contributions will be welcomed at all stages
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& Denise Adams Center for Parkinson’s Disease Research and Scherzer Neurogenomics Lab combine population-scale human data (>30,000 deeply phenotyped participants), single-cell and spatial omics, and next
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or Functional ultrasound imaging or Electrophysiology (neuropixels) in behaving animals Quantitative data analysis and computational modeling of network activity Data acquisition systems, signal processing and
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resistance mechanisms to targeted, chemo, or immunotherapies. Our long-term vision is to identify new therapeutic vulnerabilities, improve patient stratification and provide spatial proteomics data
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screening, and multi-omics data to uncover cancer vulnerabilities and enable next-generation therapeutic target discovery. The group is supported by major competitive funding, contributes actively
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Work The place of work is Ny Munkegade 120, 8000 Aarhus C. Contact Information Further information about the position may be obtained from / For further information please contact: Dr Simon Wall +45
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of the research, contributing to protocol writing, and helping to oversee the execution of the research. The postdoctoral scholar will also contribute to data analysis and write-up of scientific findings
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Tissue Resource Center, cancer biobank, the Genomic Data Commons (https://gdc-portal.nci.nih.gov/ ) and many others. Responsibilities: The applicant will have the opportunities to tackle cutting edge
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and sequence-based mechanisms of gene regulation. Current projects focus on deciphering the regulatory landscape of the human genome by integrating AI-driven enhancer modeling with GWAS data and