Postdoc on cofolding models for In-Silico Drug Discovery

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Job Function:
Career Programs

Job Sub Function:
Post Doc - Drug Discovery & Pre-Clinical/Clinical Development

Job Category:
Career Program

All Job Posting Locations:
Beerse, Antwerp, Belgium

Job Description:

Job Description

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

At Johnson & Johnson,we believe health is everything.

Our strength in healthcare innovation empowers us to build aworld where complex diseases are prevented, treated, and cured,where 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 cutting‑edge In Silico Discovery department within Therapeutics Discovery at J&J.

This position is intended for early‑career scientists with strong technical training and research interest in co‑folding models, structural bioinformatics, and AI/ML for drug discovery.

The position focuses on research and development of fine‑tuning strategies for co‑folding models applied to structure‑based drug discovery.

The role will involve generating and curating training and benchmarking datasets, evaluating task‑specific performance, and integrating fine‑tuned 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 fine‑tuning experiments for co‑folding and structure‑prediction models, including benchmarking against internal and external baselines.


* Generate, curate, and manage training and validation datasets (e.g.

complex structures, MD‑derived conformational ensembles, and project‑specific data) to support model fine‑tuning and evaluation


* Assess model performance using appropriate structural and task‑relevant metrics and contribute to b...