Post Doc

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions 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. Learn more at jnj.com.

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Job Function:

Career Programs

Job Sub Function:

Post Doc - Data Analytics & Computational Sciences

Job Category:

Career Program

All Job Posting Locations:

Beerse, Antwerp, Belgium, Leiden, Netherlands, Limerick, Ireland, Maidenhead, Berkshire, United Kingdom

Job Description:

About the Role

Johnson & Johnson Innovative Medicine is recruiting a Postdoctoral Researcher, AI Safety to join our Data, Data Science & AI organization. This is a fixed-term research appointment of two years reporting directly to the Scientific Fellow, AI Safety.

The role can be based at one of our sites in Belgium, The Netherlands, UK or Ireland.

Agentic AI is becoming central to pharmaceutical R&D-from discovery and translational science to development and regulatory work-where evidence standards are rigorous and errors can ultimately affect patient safety and outcomes. Our GenAI Platform supports that shift across a rapidly expanding population of autonomous workflows. Safety at this scale cannot be retrofitted through checks written into individual applications; it must be a property of how these systems are built.

As a Postdoctoral Fellow, you will lead a focused, publishable research program on how agentic AI in pharmaceutical R&D can be governed through provable controls, continuously tested through adversarial assurance, and made safe by construction. Your research agenda will sit within one or more of the team's three connected mandates:

• Provable controls. Develop deterministic, explainable controls that persist throughout agentic workflows, and methods to verify that they hold.
• Adversarial assurance. Advance continuous red-team and evaluation methods that test safeguards against credible failure scenarios and produce defensible evidence.
• Safety-native architecture. Investigate pre- and post-training safety alignment and defense-in-depth techniques that make agentic AI safe by construction for regulated pharmaceutical R&D.

This is a hands-on research role with real systems as the testbed. You will formulate research questions, build the prototypes and experiments that answer them, publish the results, and work with our engineering partners to carry validated methods into the platform.

Key Responsibilities

• Execute an independent research agenda (agreed with the Scientific Fellow/mentor) on controls, adversarial assurance, or safety-native architecture for agentic AI in regulated scientific settings.
• Design rigorous, reproducible experiments-including baselines, ablations, and uncertainty estimates-that test whether a safety property genuinely holds.
• Evaluate pre-training data interventions and post-training methods-including supervised fine-tuning, preference optimization, and safety tuning-for regulated scientific use cases, including whether safety properties persist under domain adaptation.
• Prototype layered architectures that constrain agent behavior, and translate scientific, quality, privacy, and regulatory requirements into testable system specifications.
• Work with platform engineering to move validated methods from prototype into the GenAI Platform, with documentation that makes results reproducible and auditable.
• Present your work to scientific, engineering, and leadership audiences, and represent the team at conferences, workshops, and standards activities.

What This Role Is Not

• Not frontier model development. We are not pre-training foundation models at scale. The research question is how alignment and architecture should be adapted so that the models and platforms available to us are safe for pharmaceutical R&D.
• Not a guardrail-prompt role. Safety here is architectural and enforcement is deterministic. A system prompt asking a model to behave is not a control.
• Not research in isolation. Your research questions come from real agentic workflows, and success includes evidence that the methods work on the