Data Science & AI Innovation Postdoctoral Fellow

Novartis•San Diego, CA

1d•$57,800 - $107,300•Onsite

About The Position

We are excited to invite applications for the Novartis Biomedical Research Postdoctoral Fellowship Program, a unique training opportunity designed for exceptional early-career scientists eager to tackle some of the most challenging problems in biomedical research and drug discovery. As a Postdoctoral Research Fellow, you will join Translational Medicine in Basel, Cambridge, or San Diego and pursue an innovative research project at the forefront of biomedical science and drug discovery. You will work alongside leading scientists in a highly collaborative, multidisciplinary environment while gaining exposure to the broader ecosystem that translates scientific discovery into medicines. Our fellows are empowered to ask bold scientific questions, apply cutting-edge technologies, and develop approaches that have the potential to transform patient care. This role aims to develop a multi-agent AI platform that enables scientists to efficiently access, analyze, and integrate knowledge from diverse biomedical data sources across the drug discovery and development continuum. The system combines specialized AI agents, domain-specific tools, and scientific data repositories to assist subject matter experts in tasks such as biomarker discovery, human genetics interpretation, omics data analysis, literature synthesis, and translational evidence generation. Rather than replacing scientific expertise, the platform is designed to augment decision-making by automating repetitive analytical tasks while maintaining a human-in-the-loop workflow. This project focuses on operationalizing scientific AI capabilities within a centralized enterprise agent platform. Unlike general-purpose AI applications, biomedical research requires agents to operate across complex, heterogeneous data modalities while adhering to stringent governance and compliance requirements. The project addresses the technical and organizational challenges of integrating specialized AI agents, scientific workflows, and knowledge assets into a secure, interoperable, and scalable framework for drug discovery and development. The position offers a unique opportunity to conduct impactful research while developing the skills necessary for a successful career in biomedical science.

Requirements

PhD (or equivalent doctoral degree) in a relevant scientific discipline completed prior to the fellowship start date.
The program is intended for scientists immediately following their PhD training (PhD conferred in 2026 only).
Demonstrated record of scientific achievement (publications, presentations, patents, or equivalent)
Strong commitment to learning, innovation, and professional development

Nice To Haves

Experience applying artificial intelligence, machine learning, computational biology, or data science methods to biomedical research, with demonstrated interest in translational medicine, drug discovery, or related domains.
Familiarity with complex biomedical data modalities (e.g., human genetics, multi-omics, clinical trial data, or biomedical literature) and an interest in developing AI-driven solutions within regulated research environments.

Responsibilities

Develop and integrate scientific AI agents into the enterprise agent platform.
Adapt and operationalize domain-specific AI agents, workflows, and knowledge assets to support biomarker discovery, translational research, human genetics, and multi-omics analysis.
Drive novel translational medicine use cases.
Identify, develop, and evaluate high-impact applications of multi-agent AI systems across translational medicine, leveraging diverse biomedical data sources to address scientific and clinical research questions.
Enable adoption of enterprise multi-agent capabilities.
Collaborate with platform and engineering teams to integrate scientific agents within existing enterprise frameworks, ensuring interoperability, scalability, and compliance with organizational standards.
Collaborate with cross-functional stakeholders.
Partner with scientists, data scientists, engineers, and platform teams to translate research needs into reusable AI-enabled workflows and scientific applications.
Establish evaluation and governance frameworks.
Develop benchmarks, validation approaches, and best practices to assess scientific utility, reliability, and responsible deployment of AI agents in drug discovery and development.