- Salary
- $82k – $92k
- Location
- USA - Massachusetts - Cambridge (320 Bent Street), United States of America
- Workplace
- Hybrid
- Type
- Full-time
- Department
- Healthcare
- Seniority
- Entry
- Education
- PhD
- Closing date
- Today
- Source
- Workday
Description
Job Description
Postdoctoral Research Fellow, Computational Pathology and Spatial AI
Within DAGS, our AI/ML team develops biologically grounded and interpretable machine learning methods to accelerate biomarker discovery across oncology. We are seeking a highly motivated Postdoctoral Research Fellow to develop next-generation multimodal AI approaches that integrate histopathology, transcriptomics, and spatial biology to characterize tumor microenvironment architecture and generate novel biological insights from routine H&E pathology images.
What You'll Do:
- Advance AI/ML methods that connect pathology images with molecular and spatial biology data.
- Develop interpretable computational approaches to study tissue organization, tumor microenvironment biology, and disease-relevant patterns.
- Integrate multimodal datasets to support biomarker discovery and biological hypothesis generation.
- Collaborate with multidisciplinary teams across data science, pathology, biology, and translational research.
- Communicate scientific findings through presentations, publications, and cross-functional discussions.
Education Requirement:
- Must currently hold a PhD OR
- Receive a Ph.D. no later than spring 2027
- PhD in Computer Science, AI/ML, Computational Biology, Bioinformatics, Biomedical Engineering, Statistics, Applied Mathematics, or a related quantitative discipline.
Required Skills and Experience:
- Computational Pathology, Deep Learning, Computer Vision, Multimodal AI, Transcriptomics, Foundation Models, Oncology, Tumor Microenvironment, Spatial Biology.
- Strong expertise in machine learning, deep learning, and data science.
- Proficiency in Python and deep learning frameworks such as PyTorch.
- Experience with computer vision, representation learning, multimodal modeling, or biomedical data analysis.
- Demonstrated research productivity through publications, preprints, or conference presentations.
- Ability to communicate complex technical concepts to multidisciplinary scientific teams.
Preferred Skills and Experience:
- Computational pathology.
- Spatial transcriptomics, RNA-seq, or single-cell omics analysis.
- Foundation models, transformers, and multimodal learning.
- Tumor microenvironment biology and oncology biomarker discovery.
- Publications in venues such as MICCAI, CVPR, NeurIPS, ISMB, AACR, or related journals.
The salary range for this role is: $82,000- $92,000
This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting. An employee’s position within the salary range will be based on several factors including, but not limited to relevant education, qualifications, certifications, experience, skills, geographic location, government requirements, and business or organizational needs.
The successful candidate will be eligible for annual bonus and long-term incentive, if applicable.
We offer a comprehensive package of benefits. Available benefits include medical, dental, vision healthcare and other insurance benefits (for employee and family), retirement benefits, including 401(k), paid holidays, vacation, and compassionate and sick days.
Required Skills:
Academic Research, Adaptability, Bioinformatics, Biological Imaging, Computer Vision, Data Analysis, Machine Learning, Scientific Research, TranscriptomicsPreferred Skills:
Current Employees apply HERE
Current Contingent Workers apply HERE
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Employee Status:
RegularRelocation:
DomesticVISA Sponsorship:
YesTravel Requirements:
10%Flexible Work Arrangements:
HybridShift:
Not IndicatedValid Driving License:
NoHazardous Material(s):
N/AJob Posting End Date:
10/12/2026*A job posting is effective until 11:59:59PM on the day BEFORE the listed job posting end date. Please ensure you apply to a job posting no later than the day BEFORE the job posting end date.