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

• 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.

Do you have a Doctoral degree?, * 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.

• Cutting-Edge Resources: Access to state-of-the-art compute and GPU infrastructure, including H100 and B300 GPU clusters.

The Laboratory of Computational Biology in Leuven (www.aertslab.org), 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.