Postdoc: Co-Folding AI for In-Silico Drug Discovery

Johnson & Johnson Innovative Medicine; Beerse; On-site; EUR 60,000 - 96,000; Full time

Benefits offered by this job

Annual bonus

Vacation days

Parental leave

Bereavement leave

Well-being reimbursement

Insurance plans

Job summary

Johnson & Johnson Innovative Medicine invites applications for a Postdoctoral Scholar in Beerse, Belgium, to advance co-folding models for In-Silico Drug Discovery. The role is in the In Silico Discovery department within Therapeutics Discovery and targets early-career scientists with strong ML and biomolecular modeling interests.

The position focuses on fine-tuning strategies, data curation, and evaluation, collaborating across CADD, biologics, and AI/ML teams to accelerate discovery while

Qualifications

• We require a PhD in a relevant field with strong computational experience.
• Experience with AI/ML model development and evaluation is essential.
• Strong coding skills in Python and familiarity with HPC environments.
• Excellent scientific communication and publication record.

Responsibilities

• Design, run, and analyze fine-tuning experiments for co-folding models.
• Generate and manage training datasets for benchmarking.
• Assess model performance using structural metrics and report results.
• Integrate fine-tuned models into discovery workflows and documentation.
• Collaborate with multiple teams to align outputs with drug-discovery needs.
• Contribute to internal publications and external collaborations.

Skills

English proficiency

Analytical thinking

Scientific communication

Team collaboration

Education

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

Tools

PyTorch

Python

HPC

Job description

Johnson & Johnson Innovative Medicine invites applications for a Postdoctoral Scholar in Beerse, Belgium, to advance co-folding models for In-Silico Drug Discovery. The role is in the In Silico Discovery department within Therapeutics Discovery and targets early-career scientists with strong ML and biomolecular modeling interests.

The position focuses on fine-tuning strategies, data curation, and evaluation, collaborating across CADD, biologics, and AI/ML teams to accelerate discovery while