Postdoc: Multimodal AI for Pathology & Spatial Biology

MSD; Cambridge (MA); Hybrid; USD 82,000 - 92,000; Full time

Benefits offered by this job

Medical, dental and vision benefits

401(k) retirement plan

Paid holidays and vacation

Job summary

Merck & Co., Inc. is seeking a Postdoctoral Research Fellow in Cambridge, MA to develop next‑generation multimodal AI approaches that integrate histopathology, transcriptomics, and spatial biology.

The role focuses on tumor microenvironment architecture and generating new biological insights from routine H&E images. The candidate will advance AI/ML methods, build interpretable models, and collaborate across data science, pathology, biology, and translational research teams.

Qualifications

• PhD required or expected by spring 2027 across quantitative field.
• Strong expertise in ML/DL and data science.
• Proficient in Python and PyTorch.
• Experience with computer vision, multimodal modeling, and biomedical data analysis.

Responsibilities

• Advance AI/ML methods that connect pathology images with molecular and spatial biology data.
• Develop interpretable computational approaches to study tissue organization and tumor microenvironment biology.
• Integrate multimodal datasets to support biomarker discovery and hypothesis generation.
• Collaborate with data science, pathology, biology, and translational research teams.
• Communicate findings through presentations, publications, and cross-functional discussions.

Skills

Computational pathology

Deep learning

Computer vision

Multimodal AI

Transcriptomics

Foundation models

Oncology

Tumor microenvironment

Spatial biology

Machine learning

Python

PyTorch

Education

PhD in Computer Science, AI/ML, Computational Biology, Bioinformatics, Biomedical Engineering, Statistics, Applied Mathematics or related quantitative discipline

Tools

PyTorch

Job description

Merck & Co., Inc. is seeking a Postdoctoral Research Fellow in Cambridge, MA to develop next‑generation multimodal AI approaches that integrate histopathology, transcriptomics, and spatial biology.

The role focuses on tumor microenvironment architecture and generating new biological insights from routine H&E images. The candidate will advance AI/ML methods, build interpretable models, and collaborate across data science, pathology, biology, and translational research teams.