(Associate) Scientist Translation Research - Single-cell Omics & AI

(Associate) Scientist Translation Research - Single-cell Omics & AI

VIB

Ghent or Leuven, Belgium

About the role

We are seeking a Computational Biology Scientist in Single-cell omics & AI to support the valorization trajectory of a computational platform combining single‑cell omics, AI machine learning, and translational biology. The role involves collaboration with academic research group(s), with a strong focus on bridging advanced computational methods to biologically meaningful outcomes. The successful candidate will contribute to the application, evaluation, and interpretation of AI models on single-cell biological datasets, while keeping downstream translation and valorization potential in clear focus.

Key responsibilities

• Apply and evaluate advanced machine‑learning approaches on single‑cell and gene regulatory data to extract biologically relevant signals and propose meaningful and testable hypotheses, grounded in disease biology;
• Perform end‑to‑end processing, quality control, integration, and analysis of single‑cell and multimodal omics datasets (e.g. scRNA‑seq, scATAC‑seq);
• Train, evaluate, and benchmark deep learning models operating on single‑cell, regulatory, or multimodal biological data;
• Support target and mechanism prioritization by integrating model predictions with biological knowledge and external data sources;
• Work closely with academic partner groups and the Innovation & Business (I&B) team to align technical development with biological relevance and translational objectives;
• Contribute to discussions on valorization pathways, including positioning of results for partnerships, licensing, or venture creation.

Your profile

• PhD (or equivalent experience) in Computational Biology, Bioinformatics, Systems Biology, Machine Learning, or a related field;
• Strong hands‑on experience with single‑cell omics analysis, particularly scRNA‑seq and/or scATAC‑seq;
• Practical experience applying machine learning or deep learning methods to biological data;
• Proficient in Python, with working knowledge of bash and experience using HPC or cluster environments (e.g. SLURM);
• A pragmatic scientist who combines technical depth with a clear translational, goal‑oriented mindset;
• Ability to critically interpret computational and bioinformatics outputs in a biological and disease‑relevant context;
• Curious, proactive, and motivated to operate at the interface of research, innovation, and valorization.

Desirable/Plus

• Hands-on experience with deep learning frameworks (e.g. PyTorch, TensorFlow) in a research context;
• Industry experience in pharma and/or biotech, particularly in translational research, data‑driven discovery, or applied computational biology;
• Knowledge of DevOps practices, software testing, or reproducible workflow development.