Summary
The Laboratory of Virology and Antiviral Research at the Rega Institute, KU Leuven, is seeking a highly motivated postdoctoral computational biology researcher. The successful candidate will join the team for the ERC Advanced Grant project ANTIVIRMAP. This initiative is carried out in close collaboration with the Bioinformatics laboratory (ESAT-STADIUS) and Leuven.AI to fundamentally revolutionize antiviral target discovery using state-of-the-art AI and advanced machine learning models.
Postdoctoral Researcher in Machine Learning, KU Leuven, Belgium
Key Job Details
| Feature | Details |
| Designation | Postdoctoral Researcher – Machine Learning |
| Vacancy Reference | BAP-2026-450 |
| Employment Type | Full-time (Voltijds) |
| Location | Leuven, Belgium |
| Contract Duration | Initially 1 year, with the possibility of extension based on positive evaluation |
| Application Deadline | August 31, 2026, 23:59 CET |
Research Area
- Primary Domains: Computational Biology, Machine Learning, Deep Learning, and Chemo-informatics.
- Focus: Uncovering the “terra incognita” of antiviral drug discovery by leveraging high-throughput, multiplex, high-content multiparametric phenotypic antiviral assays to establish a first-of-its-kind “Atlas of Druggable Antiviral Targets.”
Eligibility & Qualifications
- Education: Holds a PhD in Machine Learning, Computer Science, Bioinformatics, or an equivalent field.
- Technical Skills:
- Strong machine learning modeling expertise with experience analyzing large-scale datasets.
- Familiarity with deep learning frameworks (e.g., PyTorch or TensorFlow).
- Experience in data preparation and data fusion (generative AI, kernel methods, Bayesian modeling).
- Strong practical statistical skills (handling batch effects, confounders, experiment design).
- Preferred Assets: Experience with cellular imaging data (e.g., CellProfiler, CNNs), virology, immunology, or chemo-informatics/drug discovery.
- Soft Skills: Excellent English communication skills, a strong publication record, and the ability to work independently while leading collaborative efforts.
Job Description (Responsibilities)
- Model Development: Design, develop, and deploy advanced machine learning approaches (deep learning, active learning, Bayesian modeling) that leverage the full complexity of high-content imaging data.
- Data Integration: Combine cellular imaging data, chemical compound structures, viral genomes, and other multi-omic datasets.
- Pipeline Optimization: Take ownership of implementing and optimizing ML-driven models in the automated high-biosafety antiviral screening pipeline (CAPS-IT).
- Phenotypic Analysis: Extract and interpret detailed phenotypic fingerprints at whole-well and single-cell resolutions to cluster compounds, infer mechanisms of action, and guide compound prioritization.
- Iterative Refinement: Refine models using downstream validation data (chemo-genetics, structural modeling, thermal proteome profiling) to create an adaptive discovery pipeline.
How to Apply
Interested candidates must apply using the university’s online portal. To submit your application, use the online application tool.
Please ensure your application includes the following documents:
- Motivation Letter: Summarizing your research experience, your interest in the position, and how your skills match the desired profile.
- Curriculum Vitae (CV)
- Diplomas & Transcripts: Records for your BSc, MSc, and PhD degrees.
- References: Contact details for two professional references filled out in the application form.
- Supporting Info: Any other relevant information, such as a full publication list.
For specific questions about the role, you can reach out directly via email to Prof. dr. Johan Neyts (johan.neyts@kuleuven.be), Dr. Hendrik Jan Thibaut (hendrikjan.thibaut@kuleuven.be), or Dr. Lotte Coelmont (lotte.coelmont@kuleuven.be).
Last Date to Apply
- August 31, 2026, at 23:59 CET
