PhD student Machine learning in plant regulatory genomics
PhD student Machine learning in plant regulatory genomics
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Job Description
Job description
The Vandepoele research lab (Comparative Network Biology ) invites applications for a fullyfunded 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 largescale experimental datasets (e.g. bulk and singlecell RNASeq ChIP/DAPSeq proteinDNA interaction data bulk and singlecell ATACSeq) and the application of diverse supervised machine learning approaches (e.g. featurebased 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 contextspecific 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 VIBUGent Center for Plant Systems Biology.
Relevant publications
- 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):125137. doi: 10.1038/s
- 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.
- MINIEX: Integrative inference of singlecell gene regulatory networks in plants. Ferrari C Manosalva Prez N Vandepoele K. Mol Plant. 2022 Nov 7;15(11):. doi: 10.1016/j.molp.2022.10.016
- Charting plant gene functions in the multiomics and singlecell era. Depuydt T De Rybel B Vandepoele K. Trends Plant Sci. 2023 Mar;28(3):283296. doi: 10.1016/j.tplants.2022.09.008
Profile
Essential
- You obtained excellent grades in your bachelor and MSc degree (i.e. you belong to the top 25% of your cohort).
- Good programming (Python) and Linux/Unix/commandline skills are a must.
- You have a good understanding of molecular biology and recent RNA and DNAbased omics profiling methods.
- You are enthusiastic about computational / systems biology and want to learn more about applying machine learning for (singlecell) regulatory data analysis.
- You have good communication and writing skills and have an excellent knowledge of written and spoken English.
- You are highly motivated selfcritical you work with rigor and you work accurately.
- You combine being a team player with a strong sense of autonomy and responsibility.
- You are willing to apply for additional funding when eligible.
Desirable but not required
- Experience with machine learning (e.g. Scikitlearn/PyTorch/Tensorflow/Keras) is a plus.
- Experience with explainable AI (e.g. SHAP) is a plus.
- Experience with highperformance computing software containers is a plus.
- Knowledge about plant biology/genomics is a plus.
- Experience with software development tools (e.g. GIT/Nextflow) is a plus.
We offer
- An intellectually stimulating international and multidisciplinary research environment
- Access to stateoftheart tools and (CPU/GPU) infrastructure
- Highquality training to develop hard and soft skills (VIB and UGent Doctoral Schools)
- Opportunities to participate in (inter)national workshops and scientific conferences
- A competitive salary including benefits
The VIBUGent Center for Plant Systems Biology is a worldleading 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).
How to apply
Interested candidates are invited to submit a detailed CV (clearly mentioning your computational skills) a cover letter outlining their research interests and relevant experience academic transcripts and contact information for at least one reference.
Please submit your application via the VIB application tool.
For more information or inquiries about the position mail to
We look forward to welcoming you to our team at the VIBUGent Center for Plant Systems Biology!
Employment Type
Full Time
