Deep Learning for Glioblastoma Sequence-to-Function Models

ELLIS; Town of Belgium (WI); On-site; USD 65,000 - 85,000; Full time

Benefits offered by this job

Cutting-edge resources and GPU infrastructure

Opportunity for innovative research

Minimum of 3 years funding

Job summary

ELLIS is seeking a talented postdoctoral researcher in the United States to develop next-generation sequence-to-function models for glioblastoma. This role involves building advanced deep learning architectures and integrating multi-omics data.

The ideal candidate will have a PhD in a relevant field and strong programming skills in Python, with experience in TensorFlow or similar frameworks. Join a collaborative team and work on innovative therapeutic designs in a stimulating research environment.

Qualifications

• PhD in a relevant field with proficiency in Python.
• Strong machine learning experience with TensorFlow, Keras, or PyTorch.
• Ability to work independently within a collaborative international team.

Responsibilities

• Build and train advanced deep learning models for genomic enhancer regulation.
• Design programmable synthetic enhancers targeting cancer cell states.
• Integrate single-cell atlases with spatial transcriptomics data.

Skills

Python

Machine Learning frameworks (TensorFlow, Keras, PyTorch)

Explainable AI (SHAP, Integrated Gradients)

Education

PhD in Artificial Intelligence, Bioinformatics, Computer Science, Physics, Engineering or related field

Tools

High-performance computing (HPC)

Software containers

Job description

Deep Learning for Glioblastoma Sequence-to-Function Models

VIB.AI, the VIB Center for AI & Computational Biology, is a young research center dedicated to combining machine learning with in-depth knowledge of biological processes. Our mission is to study fundamental problems in biology and work towards foundation models of biological systems and innovative AI-driven biotech applications.

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.

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.

We offer

• Cutting‑Edge Resources: Access to state‑of‑the‑art compute and GPU infrastructure, including H100 and B300 GPU clusters.
• Innovation: The opportunity to apply a recently published, "proof‑of‑concept" method for synthetic enhancer design to a critical, real‑world clinical challenge.
• Environment: A stimulating, international research setting in a top‑tier university.
• Funding: Minimum of 3 years of funding available; candidates are encouraged to apply for prestigious fellowships (EMBO, MSCA, etc.).
• Start Date: As soon as possible.

How to apply?

Please complete the online application procedure via the VIB website and include:

• A detailed CV
• A motivation letter specifically detailing your interest in glioblastoma and deep learning

For more information: [e-mail]