PHD STUDENT: DEEP LEARNING IN GENOMICS TO DECODE CELLULAR IDENTITIES IN THE HUMAN BRAIN

Description The Yves Moreau and Stein Aerts research labs are looking for a shared PhD student to apply deep representation learning and large language models to decipher the genomic regulatory code, with a focus on the human brain.
You will engineer new AI models to predict cell-type specific expression of all protein-coding genes in the genome, across diverse cell types in different regions of the human brain.
Techniques will include Transformers, self-supervised learning, reinforcement learning, and generative AI models.
Explainability of your models is essential, to discover new biological rules of the genomic regulatory code.
As a secondary aim, your trained models can be applied to interpret human genetic variation and to improve the prediction of disease risk from the genome sequence.
The project, funded by an FWO Strategic Basic Research (SBO) grant, will focus on designing and testing genomic enhancers to target transgenes to specific cell types in the mouse brain and in cultured human brain biopsies using adeno-associated viral vectors.
Whereas you will focus on AI modeling, other members in the consortium will perform wet-lab experiments to test your predictions and designed DNA sequences.
This provides an opportunity to get acquainted with “wet-lab” experimental research.
The PhD student will be stationed at ESAT-STADIUS in the Moreau group but will be embedded in both the Moreau and the Aerts labs as well as the new VIB Center for AI & Computational Biology and the Leuven Bioinformatics and AI community.
The PhD student will be jointly supervised by Yves Moreau and Stein Aerts.
Publications Check out some of our recent publications From the Aerts lab, related to deep learning in genomics.
Minnoye & Taskiran, Genome Research ; Kalender Atak & Taskiran, Genome Research Janssens, Aibar & Taskiran, Nature Taskiran et al., BioRxiv For all publications, see www.
ertslab.
rg/#publications.
From the Moreau lab, related to deep learning.
Verplaetse et al., Genome Biology Passemiers et al., arXiv.
Oldenhof et al., arXiv.
Raimondi et al., Bioinformatics For all publications, see https://scholar.
oogle.
om/citations?hl=en&user=zWftTEUAAAAJ Profile You obtained a Master's in Computer Science, Artificial Intelligence, Bioinformatics, Physics, Engineering, Bio-engineering, or equivalent.
Please note that to be admitted to the doctoral training distinction (= cum laude) based on your study results or professional realizations is a strict requirement.
Comprehensive AI background (deep learning, probabilistic modeling, generative AI) Proficient in Python programming Experience with machine learning is a plus (e.
., PyTorch/Tensorflow/Keras) Experience with explainable AI (e.
., SHAP) is a plus Experience with high-performance computing, software containers Experience with genomics is a plus, but not essential Ability to work independently and in a team.
Proficiency in oral and written English.
We offer Access to state-of-the-art compute & GPU infrastructure A stimulating international research environment You can be engaged in our SBO consortium, bringing together research labs across the University of Leuven, University of Antwerp, and VIB Competitive salary and benefits Fully funded PhD scholarship, but encouraged to apply for a national PhD fellowships (e.
., FWO).
Starting date.
as soon as possible How to apply? Please complete the online application procedure and include a detailed CV, two reference letters, and a motivation letter.
For further information and questions, please send an email to Yves Moreau () or Stein Aerts ()