Summary
Aarhus University is inviting applications for a PhD fellowship/scholarship within the Graduate School of Technical Sciences, focusing on quantitative genetics and genomics. The successful candidate will engage in developing computational tools to enhance modern plant breeding through comparative genomics and AI methodologies.
PhD Candidate in Comparative Genomics and Machine Learning for Plant Biology, Denmark
Designation
PhD Candidate in Comparative Genomics and Machine Learning for Plant Biology
Details
| Field | Details |
|---|---|
| Research Area | Comparative Genomics and Machine Learning for Plant Biology |
| Location | C.F. Møllers Allé 3, Bldg. 1130, 8000 Aarhus C, Denmark |
| Eligibility/Qualification | – Master’s degree in Bioinformatics, Computational Biology, Computer Science, Mathematics, Physics, or a related field – Knowledge of Python or R programming – Familiarity with machine learning or deep learning methods (preferred) – Interest in plant genomics and evolutionary biology – Proficient in written and spoken English |
| Job Description | The candidate will: 1. Develop algorithms integrating evolutionary and functional information using comparative genomics and deep learning. 2. Apply frameworks across the plant kingdom to identify gene redundancy and pleiotropy. 3. Identify genes linked to key agronomic traits like flowering time and root architecture. The project will enhance skills in comparative genomics, phylogenomics, and deep learning, contributing to crop improvement. |
| How to Apply | Submit your application via the link provided under ‘how to apply’ on the Aarhus University website. A project description PDF must be uploaded, which can be copied directly from this post. |
| Last Date to Apply | 31 May 2026 by 23:59 CEST |
Contacts
For further information:
- Professor Torben Ask
Email: torben.asp@qgg.au.dk
Phone: +4587158243 - Assistant Professor Irene Julca
Email: irene.julca@qgg.au.dk
This scholarship opportunity is aimed at fostering innovation in plant biology and welcomes diverse applications from qualified candidates.
