Postdoc: Co-Folding AI for Drug Discovery

7555-Janssen Pharmaceutica NV Legal Entity; Geel; Vor Ort; EUR 60.000 - 96.000; Vollzeit

Benefits dieser Stelle

Annual bonus

Vacation days

Parental leave

Bereavement leave

Well-being reimbursement

Insurance plans

Zusammenfassung

Johnson & Johnson seeks a Postdoctoral Scholar for Co-folding models in In Silico Discovery at Beerse, Belgium. You will design fine‑tuning experiments, curate complex structural datasets, and integrate models into discovery workflows within a collaborative, multidisciplinary team.

The role emphasizes AI/ML model development, high‑level Python/PyTorch work, and strong scientific communication while enabling travel up to 10% for collaborations and conferences.

Qualifikationen

• PhD in computational chemistry, structural bioinformatics, computational biology, or machine learning applied to biomolecular systems.
• Experience with AI/ML model development, training, fine-tuning, and evaluation (e.g. PyTorch).
• Experience coding and developing research workflows in Python.
• Familiarity with HPC and GPU computing environments.

Aufgaben

• Design, run, and analyze fine‑tuning experiments for co‑folding and structure‑prediction models.
• Generate, curate, and manage training/validation datasets for model fine‑tuning and evaluation.
• Assess model performance using structural and task‑relevant metrics and report results.
• Integrate fine‑tuned models into end‑to‑end discovery workflows, including inference and documentation.
• Collaborate with In Silico Discovery scientists across CADD, biologics, and AI/ML teams to align outputs with drug‑discovery needs.

Kenntnisse

AI/ML model development

Python programming

PyTorch

HPC/GPU computing

Scientific communication

Ausbildung

PhD in computational chemistry, structural bioinformatics, computational biology, or ML for biomolecular systems

Tools

PyTorch

Python

Jobbeschreibung

Johnson & Johnson seeks a Postdoctoral Scholar for Co-folding models in In Silico Discovery at Beerse, Belgium. You will design fine‑tuning experiments, curate complex structural datasets, and integrate models into discovery workflows within a collaborative, multidisciplinary team.

The role emphasizes AI/ML model development, high‑level Python/PyTorch work, and strong scientific communication while enabling travel up to 10% for collaborations and conferences.