Postdoctoral Scientist, Oncology Foundation Models

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.

As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit.

Please note that this role is available across multiple countries and may be posted under different requisition numbers to comply with local requirements. While you are welcome to apply to any or all of the postings, we recommend focusing on the specific country(s) that align with your preferred location(s):

Remember, whether you apply to one or all of these requisition numbers, your applications will be considered as a single submission.

Johnson & Johnson Innovative Medicine is seeking a Postdoctoral Scientist, Oncology Foundation Models. There is a strong preference for the position to be based at one of our sites in Berlin, Germany; Beerse, Belgium; Spring House, PA; Cambridge, MA; Titusville, NJ; or La Jolla, CA. Remote work within the U.S. may be considered on a case-by-case basis, subject to company approval.

In this role, you will join the Data, Data Science & AI organization within Johnson & Johnson Innovative Medicine and contribute to the development and evaluation of foundation models for oncology using large-scale omics and clinical datasets. The work will focus on leveraging single-cell, spatial, and other high-dimensional molecular data to better understand tumor biology, the tumor microenvironment, and clinically relevant disease mechanisms.

You will work closely with Oncology Translational Research and cross-functional scientific teams to translate complex biological data into robust computational models and actionable insights.

• Collaborate with Oncology Translational Research to develop computational models that generate insights relevant to precision medicine and drug development.
• Analyze and integrate high-dimensional biological data, including single-cell and spatial transcriptomics, for model development, training, and validation.
• Develop and implement scalable statistical, machine learning, and deep learning approaches using large-scale transcriptomic and multimodal datasets.
• Design rigorous model evaluation strategies, including appropriate benchmarks, validation datasets, and biologically relevant performance metrics.
• Partner with experimental and translational scientists to design studies that enable robust evaluation and interpretation of computational predictions.
• Develop reproducible, modular, and scalable research code consistent with internal software engineering and data integrity standards.
• Communicate methods, model performance, and scientific findings through technical reports, presentations, and peer-reviewed publications.

• Ph.D. completed within the last three years in computer science, artificial intelligence, bioinformatics, computational biology, statistics, or a related quantitative field.
• Strong programming skills in Python; experience with R is desirable.
• Strong background in machine learning, deep learning, statistical modeling, or representation learning.
• Experience developing modular, scalable, and reusable software for computational research or AI/ML applications.
• Experience analyzing large-scale transcriptomic or other high-dimensional biological datasets.
• Demonstrated ability to develop and rigorously evaluate computational models.
• Strong research record, including peer-reviewed publications.
• Strong written and verbal communication skills, with the ability to communicate complex computational concepts to both technical and scientific audiences.

• Experience applying AI/ML methods to oncology, cancer biology, or drug discovery.
• Experience with single-cell RNA-seq, spatial transcriptomics, or multimodal biological datasets.
• Experience with foundation models, representation learning, transformers, or other large-scale deep learning architectures for biological data.
• Familiarity with cell therapy discovery and development.
• Experience working at the interface of computational modeling and experimental or translational biology.

PG25, Posted Internally, November 12th - November 27th

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