Postdoc on cofolding models for In-Silico Drug Discovery
Johnson & Johnson · Antwerp, Belgium
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Role summary
Postdoctoral position in computational drug discovery focused on developing and optimizing cofolding models for structure-based drug design. The role involves dataset curation, model fine-tuning, performance evaluation, and integration of AI/ML approaches into discovery workflows within a multidisciplinary research environment.
Where this listing comes from
This listing was compiled by RoleLantern from the employer's publicly available posting on Workday, captured and rephrased by RoleLantern using automated (AI) tools on 2026-10-06 (originally posted 2026-10-06). It was not submitted or confirmed by the employer through RoleLantern. Details may be inaccurate, incomplete, outdated, or no longer available; please confirm with the employer's official posting before applying.
What they're looking for
- PhD in computational chemistry, structural bioinformatics, computational biology, or machine learning on biomolecular systems
- Strong knowledge of protein structure and structure-based modeling principles
- Experience with AI/ML model development, training, fine-tuning, and evaluation using frameworks like PyTorch
- Experience generating or curating structural datasets including protein complexes and molecular dynamics data
- Python programming proficiency for research workflow development
- Familiarity with HPC and GPU computing environments
- First-author peer-reviewed publications and presentations at major scientific meetings required
What you'll be doing
- Design, execute, and analyze fine-tuning experiments for cofolding and structure-prediction models with benchmarking
- Generate, curate, and manage training datasets including complex structures and molecular dynamics ensembles
- Evaluate model performance using structural and task-relevant metrics to establish evaluation best practices
- Integrate fine-tuned models into end-to-end discovery workflows with inference, reporting, and documentation
- Collaborate with computational, CADD, biologics, and AI/ML scientists to align research with drug-discovery objectives
Privacy & handoff
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