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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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, collaborating with researchers, policymakers, industry partners, and farmers who all work on translating complex data and modelling results into actionable insights. Key responsibilities Develop and apply
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and analysis of psychophysiological, e.g., ECG, photoplethysmography, behavioural, e.g., interoception, and self-report data, e.g., interoception, somatic symptoms, mental health, from different sources
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( Nature 2024 , JACS 2026, Nature Protocols 2026). The proposed data rich experimentation will connect chemical structures to optimal reaction conditions. A competitive candidate will have a background in
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with industrial partners for process development Supporting the management of research and data infrastructure YOUR QUALIFICATIONS Research experience in macromolecular synthesis, and soft matter physics
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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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of the sequence of events around the time of the Great Oxidation Event, Link model results to proxy data, in particular from the GOE-DEEP cores, present the results at international conferences and publish them in
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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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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