Postdoc: Multimodal AI for Predictive Biology

Biopharma Careers; Cambridge (MA); On-site; USD 92,000 - 111,000; Full time

Job summary

Johnson & Johnson Innovative Medicine is seeking a Postdoctoral Scientist in Multimodal Representation Learning for Predictive Biology to advance AI/ML analytics for drug discovery. The role focuses on integrating high-content imaging, omics, and phenomics to model perturbed cellular systems.

The successful candidate will build and benchmark AI methods, publish findings, and collaborate with multidisciplinary teams across Cambridge, MA and other sites.

Qualifications

• Ph.D. completed within the last 3 years or to be completed soon.
• Strong AI/ML expertise with publications in top venues.
• Experience with biological data (Cell Painting, omics) and modeling.

Responsibilities

• Conceive, design, develop, implement and validate innovative AI/ML solutions for drug discovery problems using multimodal data of perturbed cells.
• Analyze and extract novel biological insights from large-scale, heterogeneous high-dimensional data.
• Develop and evaluate computer vision solutions to quantify heterogeneity of perturbed cells.
• Collaborate with cross-functional teams to advance algorithms and product development.
• Communicate complex methodologies and findings to diverse audiences.
• Maintain documentation and code repositories.
• Draft manuscripts and disseminate research findings.

Skills

Representation learning

Multimodal integration

Computational biology

Imaging microscopy

Foundation models

Multimodal modeling

Transformers

CNNs

Graph networks

Self-supervised learning

Multi-instance learning

Communication

Education

Ph.D. in Electrical Engineering, Biomedical Engineering, Computer Science, Applied Mathematics, or related major

Tools

Python

PyTorch

TensorFlow

Job description

Johnson & Johnson Innovative Medicine is seeking a Postdoctoral Scientist in Multimodal Representation Learning for Predictive Biology to advance AI/ML analytics for drug discovery. The role focuses on integrating high-content imaging, omics, and phenomics to model perturbed cellular systems.

The successful candidate will build and benchmark AI methods, publish findings, and collaborate with multidisciplinary teams across Cambridge, MA and other sites.