PhD scholarship in fungal evolution and innovation - BRIGHT
On-siteKongens Lyngby, Capital Region, Denmark
Job Summary
Design and execute experiments in fungal genetic engineering, adaptive laboratory evolution, and multi-omics analyses using Aspergillus flavus and Aspergillus oryzae. Generate, process, and analyze experimental data to link epigenetic regulation, genome dynamics, and evolutionary outcomes, while establishing causal mechanistic links between epigenetic states and adaptive genetic change. Visualize results, publish findings in high-impact journals, and present work at international conferences. Collaborate with researchers across disciplines and external partners to develop novel starter cultures and fermentation processes that support sustainable food systems.
Required Qualifications
- two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree
- Hands-on experience in mycology
Desired Qualifications
- Experience with fungal genetic engineering
- Experience generating genomic and transcriptomic data from fungi
- Experience generating and analying metabolomics data
- A strong interest in fungal biology, evolutionary theory, ecology and evolution, and food innovation
- Demonstrate a high level of motivation, curiosity, and scientific integrity
- Are able to work independently and take ownership of your research
- Take a proactive approach to identifying opportunities and solving problems
- Learn quickly and adapt effectively to new methods and challenges
- Have strong communication skills, including scientific writing and oral presentations in English
- Enjoy working in collaborative and interdisciplinary teams
- Are organized, structured, and committed to maintaining high-quality documentation and reproducible research practices
- Are curious and enthusiastic and playful
- aspire to become independent scientists
- eager to develop a broad and modern research toolkit spanning mycology, genetic engineering, adaptive laboratory evolution and multi-omics data generation
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