Postdoc on cofolding models for In-Silico Drug Discovery

At Johnson & Johnsonwe believe health is everything. Our strength in healthcare innovation empowers us to build aworld where complex diseases are prevented treated and curedwhere treatments are smarter and less invasive andsolutions are our expertise in Innovative Medicine and MedTech we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow and profoundly impact health for more at .

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Job Description:

We are currently recruiting for a Postdoc on cofolding models for In-Silico Drug Discovery based in Beerse Belgium.

At Johnson & Johnsonwe believe health is everything. Our strength in healthcare innovation empowers us to build aworld where complex diseases are prevented treated and curedwhere treatments are smarter and less invasive andsolutions are personal. Through our expertise in Innovative Medicine and MedTech we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow and profoundly impact health for humanity.

We are seeking a Postdoctoral Scholar to join our cuttingedge In Silico Discovery department within Therapeutics Discovery at J&J. This position is intended for earlycareer scientists with strong technical training and research interest in cofolding models structural bioinformatics and AI/ML for drug discovery.

The position focuses on research and development of finetuning strategies for cofolding models applied to structurebased drug discovery. The role will involve generating and curating training and benchmarking datasets evaluating taskspecific performance and integrating finetuned models into discovery workflows. The Postdoctoral Scholar will work closely with computational scientists in a collaborative multidisciplinary research environment.

Responsibilities of the Postdoctoral Scholar will include:

• Design run and analyze finetuning experiments for cofolding and structureprediction models including benchmarking against internal and external baselines.
• Generate curate and manage training and validation datasets (e.g. complex structures MDderived conformational ensembles and projectspecific data) to support model finetuning and evaluation
• Assess model performance using appropriate structural and taskrelevant metrics and contribute to best practices for model evaluation
• Integrate finetuned cofolding models into endtoend discovery workflows including inference reporting and documentation
• Collaborate closely with In Silico Discovery scientists across CADD biologics and AI/ML teams to align research outputs with drugdiscovery needs
• Contribute to internal scientific communication documentation and publicationready summaries of research outcomes
• Participate in external collaborations and consortia related to cofolding and structural biology AI supporting integration of research deliverables
• Engage in professional development through training courses conferences and internal scientific meetings as aligned with project milestones

Required:

• PhD in computational chemistry structural bioinformatics computational biology or machine learning applied to biomolecular systems.
• Strong understanding of protein structure and structurebased modeling concepts.
• Experience with AI/ML model development training finetuning and evaluation (e.g. PyTorch or similar frameworks).
• Experience generating or working with structural datasets (e.g. proteinligand or proteinprotein complexes MDderived data).
• Experience coding and developing research workflows in Python
• Familiarity with HPC and GPU computing environments.
• Strong analytical problemsolving and scientific communication skills.
• Track record of scientific deliveries including peer reviewed first-author publications and presentations at major national meetings is required.
• Professional fluency in English.
• Up to 10% travel both domestically and internationally is required.

Preferred skills:

• Postdoctoral or doctoral research exposure to cofolding models structural AI or related foundation models.
• Experience benchmarking ML models for scientific or drugdiscovery applications
• Familiarity with proteinligand or antibodyantigen modeling docking and virtual screening concepts.
• Experience with reproducible research practices (version control testing documentation).
• Exposure to drugdiscovery workflows or collaboration with medicinal chemistry or biologics teams.
• Experience working within academicindustry research collaborations or consortia.
• Familiarity with cofolding models or structureprediction approaches applied to biomolecular systems.

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The anticipated base pay range for this position is:

Benefits:

In addition to base pay we offer the following benefits*: an annual bonus with set target (% of pay) depending on pay grade / location where the actual amount is based on the employees and companies performance of the previous calendar year or sales commissions. Moreover we offer vacation days parental leave for a minimum of 12 weeks bereavement leave caregiver leave volunteer leave well-being reimbursement programs for financial physical and mental health. We also offer service anniversary and recognition awards and subject to the terms of their respective plans employees - and in some locations eligible dependents - can participate in several insurance plans. For more information visit Employee benefits Supporting well-being & career growth Johnson & Johnson Careers.

*This is for informative purposes only. Amounts and actual benefits may vary by location and are subject to change.