Associate Scientist, Post Doc Fellow, Computational Pathology & Spatial AI

MSD

Confirmed live yesterday High trust
Closes in 4 days Hybrid

Quick summary

Work type
Hybrid
Location
Cambridge, MA
Salary
$82,000–$92,000 / yr
Employment
Full-time
Posted
8 days ago
Freshness
Confirmed live yesterday
Closes
Oct 12, 2026 (soon)

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $217k
This role $87k
$60k most similar roles pay here $286k

This role pays less than 98% of similar roles. Most pay $179,325–$254,750 — the shaded band above. At the midpoint, this role pays about $87k versus about $217k for comparable roles.

Based on 240 similar postings.

Employer

About MSD

MSD (Merck Sharp & Dohme) is the international name for Merck & Co., a major U.S.-based pharmaceutical company.

MSD currently has 7 open roles on FindRole.

Listed pay typically runs $87,300–$137,400 across 7 roles with salary data.

Most-posted roles

View all roles at MSD

At a glance

TL;DR · Associate Scientist, Post Doc Fellow, Computational Pathology & Spatial AI

Associate Scientist, Post Doc Fellow- Computational Pathology & Spatial AI will join the DAGS team to develop next-generation multimodal AI approaches that integrate histopathology, transcriptomics, and spatial biology. The role focuses on creating interpretable machine learning methods to characterize tumor microenvironment architecture and generate biological insights from routine H&E pathology images. You will build computational models to connect pathology images with molecular data, support biomarker discovery, and communicate findings through publications and cross-functional discussions. Required expertise includes deep learning, computer vision, representation learning, and multimodal modeling using Python and PyTorch. The work involves solving complex problems in oncology by analyzing tissue organization and disease-relevant patterns. Preferred skills include experience with foundation models, transformers, spatial transcriptomics, RNA-seq, and single-cell omics analysis to advance the understanding of tumor microenvironment biology and accelerate biomarker discovery across oncology.

What you'll do

  • Develop multimodal AI models integrating histopathology, transcriptomics, and spatial biology data.
  • Create interpretable machine learning methods to study tissue organization and tumor microenvironment architecture.
  • Identify novel biological insights and biomarkers from routine H&E pathology images.
  • Build computational approaches to analyze disease-relevant patterns in cancer research.
  • Develop advanced computer vision and representation learning models for biomedical data analysis.
  • Communicate complex technical findings through scientific publications and presentations.
  • Translate multi-modal datasets into actionable biological hypotheses for oncology research.

What we're looking for

  • Must hold a PhD or be on track to receive one by spring 2027 in a quantitative field such as Computer Science, AI/ML, or Bioinformatics.
  • Expertise is required in machine learning, deep learning, and data science.
  • Proficiency in Python and deep learning frameworks like PyTorch is required.
  • Experience with computer vision, representation learning, multimodal modeling, or biomedical data analysis is required.
  • Demonstrated research productivity through publications, preprints, or conference presentations is required.
  • Ability to communicate complex technical concepts to multidisciplinary scientific teams is required.
  • Knowledge of spatial transcriptomics, RNA-seq, or single-cell omics analysis (preferred).
  • Experience with foundation models, transformers, and oncology biomarker discovery (preferred).

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