Job description
The Vandepoele research lab (Comparative Network Biology - ) invites applications for a fully-funded PhD project for 1 year and extended with another 3 years (after positive evaluation) to study regulatory genomics in plants using different computational and -omics approaches .
Starting from large-scale experimental datasets (e.g., bulk and single-cell RNA-Seq, ChIP/DAP-Seq protein-DNA interaction data, bulk, and single-cell ATAC-Seq) and the application of diverse supervised machine learning approaches (e.g., feature-based, deep learning, and explainable artificial intelligence), the goal of the project is to unravel the complex regulatory code controlling gene expression in the multicellular model plant Arabidopsis thaliana.
Together with your colleagues in the Vandepoele lab, sharing complementary expertise in plant biology, genomics, and informatics, you will focus on characterizing the regulatory sequences driving context-specific gene expression in different organs, cell types, and stress conditions. Experimental validation of your computational results will be performed through collaboration with wet lab colleagues in the VIB-UGent Center for Plant Systems Biology.
Relevant publications
1. Obtaining genetics insights from deep learning via explainable artificial intelligence. Novakovsky G, Dexter N, Libbrecht MW, Wasserman WW, Mostafavi S. Nat Rev Genet. 2023 Feb;24(2):125-137. doi: 10.1038/s41576-022-00532-2
2. Predicting transcriptional responses to heat and drought stress from genomic features using a machine learning approach in rice. Smet D, Opdebeeck H, Vandepoele K. Front Plant Sci. 2023 Jul 17;14:1212073. doi: 10.3389/fpls.2023.1212073
3. MINI-EX: Integrative inference of single-cell gene regulatory networks in plants. Ferrari C, Manosalva Pérez N, Vandepoele K. Mol Plant. 2022 Nov 7;15(11):1807-1824. doi: 10.1016/j.molp.2022.10.016
4. Charting plant gene functions in the multi-omics and single-cell era. Depuydt T, De Rybel B, Vandepoele K. Trends Plant Sci. 2023 Mar;28(3):283-296. doi: 10.1016/j.tplants.2022.09.008
Profile
Essential
5. You obtained excellent grades in your bachelor and MSc degree (i.e., you belong to the top 25% of your cohort).
6. Good programming (Python) and Linux/Unix/command-line skills are a must.
7. You have a good understanding of molecular biology and recent RNA and DNA-based -omics profiling methods.
8. You are enthusiastic about computational / systems biology and want to learn more about applying machine learning for (single-cell) regulatory data analysis.
9. You have good communication and writing skills and have an excellent knowledge of written and spoken English.
10. You are highly motivated, self-critical, you work with rigor, and you work accurately.
11. You combine being a team player with a strong sense of autonomy and responsibility.
12. You are willing to apply for additional funding when eligible.
Desirable but not required
13. Experience with machine learning (e.g., Scikit-learn/PyTorch/Tensorflow/Keras) is a plus.
14. Experience with explainable AI (e.g., SHAP) is a plus.
15. Experience with high-performance computing, software containers is a plus.
16. Knowledge about plant biology/genomics is a plus.
17. Experience with software development tools (e.g., GIT/Nextflow) is a plus.
We offer
18. An intellectually stimulating, international and multi-disciplinary research environment
19. Access to state-of-the-art tools and (CPU/GPU) infrastructure
20. High-quality training to develop hard and soft skills (VIB and UGent Doctoral Schools)
21. Opportunities to participate in (inter)national workshops and scientific conferences
22. A competitive salary including benefits
The VIB-UGent Center for Plant Systems Biology is a world-leading science institution on the Techlane Campus in Ghent, Belgium. Ghent University is among the top 100 global universities according to several international rankings.
Tentative starting date: August 2024 (to be discussed).