Postdoc Position: Deep Learning for Glioblastoma Sequence-to-Function Models

Postdoc Position: Deep Learning for Glioblastoma Sequence-to-Function Models

VIB

Leuven, Belgium

The Laboratory of Computational Biology in Leuven, led by VIB.AI Scientific Director Stein Aerts, is seeking a talented postdoctoral researcher to develop next-generation sequence-to-function models for glioblastoma (GBM). Glioblastoma is the most aggressive form of brain cancer, characterized by diverse and dynamic cell states that drive treatment resistance and poor prognosis.

In this project, funded by the Foundation Against Cancer, you will move beyond descriptive genomics to decipher the underlying regulatory logic of GBM. By leveraging single-cell multi-omics (scATAC-seq, scRNA-seq) and spatial omics, you will map enhancer landscapes at unprecedented resolution. The core innovation of your work will be integrating this data to train deep learning models that predict chromatin accessibility and gene expression patterns. These models will ultimately be used to design synthetic enhancers tailored to modulate specific GBM cell states, offering a path toward highly targeted oncolytic virus therapies and immunomodulatory interventions.

Responsibilities

• Model Development: Build and train advanced deep learning architectures (e.g., CNNs, Transformers, Generative Models) to decode the regulatory logic of genomic enhancers in GBM and the tumor microenvironment.
• Synthetic Design: Use sequence-to-function models to design "programmable" synthetic enhancers capable of targeting specific cancer cell states or host cells.
• Data Integration: Integrate pan-cancer single-cell atlases with spatial transcriptomics to understand signaling pathways and gene-regulatory dynamics.
• Explainable AI (XAI): Ensure models provide mechanistic insights into cancer cell states, moving from "black box" predictions to biological understanding.
• Collaboration: Work within a multi-disciplinary team and potentially engage with collaborators across Belgian universities.

Profile

• Education: PhD in Artificial Intelligence, Bioinformatics, Computer Science, Physics, Engineering, or a related field.
• Programming: Proficient in Python.
• Machine Learning: Strong experience with frameworks like TensorFlow, Keras, or PyTorch.
• Preferred Skills:
  • Experience with Explainable AI (e.g., SHAP, Integrated Gradients).
  • Familiarity with high-performance computing (HPC) and software containers.
  • Knowledge of cancer genomics or regulatory biology is a plus.
• Mindset: Ability to work independently while thriving in a collaborative, international team.