Summer 2026 - Digital Pathology Internship
Bristol-Myers Squibb
Summer 2026 - Digital Pathology Internship
Job Description
Curate and preprocess histopathology datasets (H&E and/or multiplex immunofluorescence) using a mix of open-source and commercial digital pathology tools Apply and adapt open-source deep learning models for tasks such as tissue and cell segmentation, classification, and multi-modal image registration Visualize and interpret model outputs through heatmaps, overlays, or other interpretable AI methods Collaborate with cross-functional teams to validate model results and assess biological significance Document workflows, results, and code according to best practices for reproducible research Present findings and project progress to scientific peers through both presentations and written reports Work-life programs include paid national holidays and optional holidays, Global Shutdown Days between Christmas and New Year's holiday, up to 120 hours of paid vacation, up to two (2) paid days to volunteer, sick time off, and summer hours flexibility. Graduate student (M.S. or Ph.D.) in biomedical engineering, bioinformatics, computer science or related fields Experience with training, validating and refining image based deep-learning models Strong programming skills in Python especially for image analysis tasks, familiarity with deep learning frameworks such as PyTorch, TensorFlow Knowledge of computer vision techniques such as segmentation, registration, ideally applied to biomedical or microscopy images Understanding of or interest in oncology, pathology, and spatial biology; prior exposure to histopathology data is a plus Familiarity with digital pathology software (e.g., QuPath, HALO, or VisioPharm) is desirable but not required Strong problem-solving ability, excellent communication skills and willingness to work collaboratively across disciplines All candidates must be authorized to work in the US both at the time of hire and for the duration of their employment. Please note that immigration or visa sponsorship is not available for this position.
Key Responsibilities
- Curate and preprocess histopathology datasets (H&E and/or multiplex immunofluorescence) using a mix of open-source and commercial digital pathology tools
- Apply and adapt open-source deep learning models for tasks such as tissue and cell segmentation, classification, and multi-modal image registration
- Visualize and interpret model outputs through heatmaps, overlays, or other interpretable AI methods
- Collaborate with cross-functional teams to validate model results and assess biological significance
- Document workflows, results, and code according to best practices for reproducible research
- Present findings and project progress to scientific peers through both presentations and written reports
Required Qualifications
- Graduate student (M.S. or Ph.D.) in biomedical engineering, bioinformatics, computer science, or related fields
- Experience with training, validating, and refining image-based deep-learning models
- Strong programming skills in Python, especially for image analysis tasks
- Familiarity with deep learning frameworks such as PyTorch, TensorFlow
- Knowledge of computer vision techniques such as segmentation, registration, ideally applied to biomedical or microscopy images
- All candidates must be authorized to work in the US both at the time of hire and for the duration of their employment
Preferred Qualifications
- Understanding of or interest in oncology, pathology, and spatial biology
- Prior exposure to histopathology data
- Familiarity with digital pathology software (e.g., QuPath, HALO, or VisioPharm)
Skills Required
- Strong problem-solving ability
- Excellent communication skills
- Willingness to work collaboratively across disciplines
Benefits & Perks
- Paid national holidays and optional holidays
- Global Shutdown Days between Christmas and New Year's holiday
- Up to 120 hours of paid vacation
- Up to two (2) paid days to volunteer
- Sick time off
- Summer hours flexibility
Additional Requirements
- No immigration or visa sponsorship available for this position
Locations
- Brisbane CA, United States
Salary
Estimated Salary Rangemedium confidence
30,000 - 45,000 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Strong problem-solving abilityintermediate
- Excellent communication skillsintermediate
- Willingness to work collaboratively across disciplinesintermediate
Required Qualifications
- Graduate student (M.S. or Ph.D.) in biomedical engineering, bioinformatics, computer science, or related fields (experience)
- Experience with training, validating, and refining image-based deep-learning models (experience)
- Strong programming skills in Python, especially for image analysis tasks (experience)
- Familiarity with deep learning frameworks such as PyTorch, TensorFlow (experience)
- Knowledge of computer vision techniques such as segmentation, registration, ideally applied to biomedical or microscopy images (experience)
- All candidates must be authorized to work in the US both at the time of hire and for the duration of their employment (experience)
Preferred Qualifications
- Understanding of or interest in oncology, pathology, and spatial biology (experience)
- Prior exposure to histopathology data (experience)
- Familiarity with digital pathology software (e.g., QuPath, HALO, or VisioPharm) (experience)
Responsibilities
- Curate and preprocess histopathology datasets (H&E and/or multiplex immunofluorescence) using a mix of open-source and commercial digital pathology tools
- Apply and adapt open-source deep learning models for tasks such as tissue and cell segmentation, classification, and multi-modal image registration
- Visualize and interpret model outputs through heatmaps, overlays, or other interpretable AI methods
- Collaborate with cross-functional teams to validate model results and assess biological significance
- Document workflows, results, and code according to best practices for reproducible research
- Present findings and project progress to scientific peers through both presentations and written reports
Benefits
- general: Paid national holidays and optional holidays
- general: Global Shutdown Days between Christmas and New Year's holiday
- general: Up to 120 hours of paid vacation
- general: Up to two (2) paid days to volunteer
- general: Sick time off
- general: Summer hours flexibility
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Answer 10 quick questions to check your fit for Summer 2026 - Digital Pathology Internship @ Bristol-Myers Squibb.
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Summer 2026 - Digital Pathology Internship
Bristol-Myers Squibb
Summer 2026 - Digital Pathology Internship
Job Description
Curate and preprocess histopathology datasets (H&E and/or multiplex immunofluorescence) using a mix of open-source and commercial digital pathology tools Apply and adapt open-source deep learning models for tasks such as tissue and cell segmentation, classification, and multi-modal image registration Visualize and interpret model outputs through heatmaps, overlays, or other interpretable AI methods Collaborate with cross-functional teams to validate model results and assess biological significance Document workflows, results, and code according to best practices for reproducible research Present findings and project progress to scientific peers through both presentations and written reports Work-life programs include paid national holidays and optional holidays, Global Shutdown Days between Christmas and New Year's holiday, up to 120 hours of paid vacation, up to two (2) paid days to volunteer, sick time off, and summer hours flexibility. Graduate student (M.S. or Ph.D.) in biomedical engineering, bioinformatics, computer science or related fields Experience with training, validating and refining image based deep-learning models Strong programming skills in Python especially for image analysis tasks, familiarity with deep learning frameworks such as PyTorch, TensorFlow Knowledge of computer vision techniques such as segmentation, registration, ideally applied to biomedical or microscopy images Understanding of or interest in oncology, pathology, and spatial biology; prior exposure to histopathology data is a plus Familiarity with digital pathology software (e.g., QuPath, HALO, or VisioPharm) is desirable but not required Strong problem-solving ability, excellent communication skills and willingness to work collaboratively across disciplines All candidates must be authorized to work in the US both at the time of hire and for the duration of their employment. Please note that immigration or visa sponsorship is not available for this position.
Key Responsibilities
- Curate and preprocess histopathology datasets (H&E and/or multiplex immunofluorescence) using a mix of open-source and commercial digital pathology tools
- Apply and adapt open-source deep learning models for tasks such as tissue and cell segmentation, classification, and multi-modal image registration
- Visualize and interpret model outputs through heatmaps, overlays, or other interpretable AI methods
- Collaborate with cross-functional teams to validate model results and assess biological significance
- Document workflows, results, and code according to best practices for reproducible research
- Present findings and project progress to scientific peers through both presentations and written reports
Required Qualifications
- Graduate student (M.S. or Ph.D.) in biomedical engineering, bioinformatics, computer science, or related fields
- Experience with training, validating, and refining image-based deep-learning models
- Strong programming skills in Python, especially for image analysis tasks
- Familiarity with deep learning frameworks such as PyTorch, TensorFlow
- Knowledge of computer vision techniques such as segmentation, registration, ideally applied to biomedical or microscopy images
- All candidates must be authorized to work in the US both at the time of hire and for the duration of their employment
Preferred Qualifications
- Understanding of or interest in oncology, pathology, and spatial biology
- Prior exposure to histopathology data
- Familiarity with digital pathology software (e.g., QuPath, HALO, or VisioPharm)
Skills Required
- Strong problem-solving ability
- Excellent communication skills
- Willingness to work collaboratively across disciplines
Benefits & Perks
- Paid national holidays and optional holidays
- Global Shutdown Days between Christmas and New Year's holiday
- Up to 120 hours of paid vacation
- Up to two (2) paid days to volunteer
- Sick time off
- Summer hours flexibility
Additional Requirements
- No immigration or visa sponsorship available for this position
Locations
- Brisbane CA, United States
Salary
Estimated Salary Rangemedium confidence
30,000 - 45,000 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Strong problem-solving abilityintermediate
- Excellent communication skillsintermediate
- Willingness to work collaboratively across disciplinesintermediate
Required Qualifications
- Graduate student (M.S. or Ph.D.) in biomedical engineering, bioinformatics, computer science, or related fields (experience)
- Experience with training, validating, and refining image-based deep-learning models (experience)
- Strong programming skills in Python, especially for image analysis tasks (experience)
- Familiarity with deep learning frameworks such as PyTorch, TensorFlow (experience)
- Knowledge of computer vision techniques such as segmentation, registration, ideally applied to biomedical or microscopy images (experience)
- All candidates must be authorized to work in the US both at the time of hire and for the duration of their employment (experience)
Preferred Qualifications
- Understanding of or interest in oncology, pathology, and spatial biology (experience)
- Prior exposure to histopathology data (experience)
- Familiarity with digital pathology software (e.g., QuPath, HALO, or VisioPharm) (experience)
Responsibilities
- Curate and preprocess histopathology datasets (H&E and/or multiplex immunofluorescence) using a mix of open-source and commercial digital pathology tools
- Apply and adapt open-source deep learning models for tasks such as tissue and cell segmentation, classification, and multi-modal image registration
- Visualize and interpret model outputs through heatmaps, overlays, or other interpretable AI methods
- Collaborate with cross-functional teams to validate model results and assess biological significance
- Document workflows, results, and code according to best practices for reproducible research
- Present findings and project progress to scientific peers through both presentations and written reports
Benefits
- general: Paid national holidays and optional holidays
- general: Global Shutdown Days between Christmas and New Year's holiday
- general: Up to 120 hours of paid vacation
- general: Up to two (2) paid days to volunteer
- general: Sick time off
- general: Summer hours flexibility
Tags & Categories
Answer 10 quick questions to check your fit for Summer 2026 - Digital Pathology Internship @ Bristol-Myers Squibb.
10 Questions
~2 Minutes
Instant Score
Related Books and Jobs