Postdoctoral Researcher – Machine Learning

Organisation/Company KU LEUVEN Research Field Computer science » Modelling tools Computer science » Programming Computer science » Systems design Technology » Computer technology Technology » Dating techniques Technology » Information technology Researcher Profile Recognised Researcher (R2) Final date to receive applications 31 Aug 2026 - 23:59 (UTC) Country Belgium Type of Contract To be defined Job Status Full-time Offer Starting Date 1 Oct 2026 Is the job funded through the EU Research Framework Programme?

Horizon Europe - ERC Reference Number BAP
- Is the Job related to staff position within a Research Infrastructure? No

Antiviral drugs are used to successfully treat infections such as with HIV and HCV. Yet, for most (life)-threatening and neglected infections, there are no such drugs. This leaves also critical gaps in epi- and pandemic preparedness. Antiviral drug discovery efforts typically focus on a few known targets. Yet, the biology of viral replication consists of many more complex processes that should harbour a wealth of undiscovered druggable targets.

Thus, a large space of potential druggable biology is entirely ignored. We aim to fundamentally revolutionise antiviral target-discovery by uncovering this terra incognita. To that end, we developed high-throughput, multiplex, high-content multiparametric phenotypic antiviral assays. These allow to screen hundreds of thousands of molecules in our fully automated high biosafety screening facility CAPS-IT against multiple viruses. You will be responsible for the development and deployment of advanced machine learning models that leverage the full complexity of the imaging data and that allow the selection of molecules that will serve for in-depth virological studies.

Ultimately, this will result in the establishment of the first-of-its-kind "Atlas of Druggable Antiviral Targets".

You will join a dynamic, multidisciplinary and international virology-team with state-of-the-art infrastructure, but will at the same time also be embedded in a team with extensive expertise in AI and machine learning for computational biology and chemo-informatics. This will provide the opportunity to design novel machine learning approaches that leverage state-of-the-art AI methods (deep learning, generative AI, Bayesian modelling, active learning, etc.)

to combine cellular imaging data, chemical compound structure, viral genomes and other omics data.

You will take ownership of the implementation and optimisation of ML-driven models in our antiviral screening pipeline thereby unlocking the full richness of the multi-parameter data using advanced AI. You will extract and interpret fully detailed phenotypic fingerprints at whole-well and single-cell resolution in virus-infected cell cultures. AI models will be used to cluster compounds and infer possible mechanisms of action, identify peculiar activity signatures and integrate cellular toxicity profiles to reduce false positives and guide compound prioritisation.

The models will be populated and iteratively refined by converging evidence from downstream validation (such as chemo-genetics, structural modelling, functional assays, thermal proteome profiling and omni-omics), creating an adaptive and constantly evolving discovery pipeline. AI-driven interpretation will exploit the full complexity of the dataset to expand the druggable antiviral target space. We seek a researcher with strong machine learning modelling expertise with experience in the analysis of challenging large-scale data sets.

Experience with cellular imaging data or virology/immunology are a plus.

• Experience in creating and evaluating machine learning models.
• Familiarity with deep learning framework, such as PyTorch or Tensorflow.
• Experience in data preparation, preferably in a bioinformatics context (data cleaning, filtering, etc.).
• Expertise in data fusion and relevant algorithms (deep learning, generative AI, kernel methods, Bayesian methods).
• Preferably, experience with high-content imaging or cell imaging data (e.g., Cell Profiler, convolutional neural networks).
• Knowledge of chemo-informatics and drug discovery.
• Strong…