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Internship Computational Imaging Scientist Jobs (NOW HIRING)

The Assistant Research Scientist will participate in research activities related to advanced ... Stay current with emerging developments in MRI, computational imaging, artificial intelligence, and ...

Display Research Scientist

Redmond, WA · On-site

$154K - $217K/yr

... computational imaging • Experience with AR display systems and understanding of their optical requirements • Demonstrated experience to identify core research problems and drive them to solution ...

Our scientists have experience designing, modeling, and prototyping advanced display systems. This ... computational imaging * Experience with AR display systems and understanding of their optical ...

The new Computational Sciences Center of Excellence (CoE) is a strategic, unified group whose goal ... Imaging Data Insights, within the Computational Biology and Medicine (CBM) department, turns ...

Interest in or exposure to imaging sciences, computational research, artificial intelligence, or related scientific fields. * Prior experience supervising administrative staff in an academic or ...

... Imaging Sciences and AI Research and Founding Director of the Center for Computational and AI-enabled Imaging Sciences (C2AIS). This individual will provide high-level leadership for the ...

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Internship Computational Imaging Scientist information

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$50.5K

$111.3K

$137.5K

How much do internship computational imaging scientist jobs pay per year?

As of Aug 8, 2026, the average yearly pay for internship computational imaging scientist in the United States is $111,343.00, according to ZipRecruiter salary data. Most workers in this role earn between $94,500.00 and $137,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an internship computational imaging scientist, and why are they important?

To thrive as an Internship Computational Imaging Scientist, you typically need a background in computer science, electrical engineering, or a related field with knowledge of image processing, algorithms, and mathematics. Familiarity with programming languages such as Python or MATLAB, and experience using libraries like OpenCV or TensorFlow, are commonly required, along with coursework or certifications in computational imaging. Strong problem-solving skills, attention to detail, and the ability to communicate technical findings effectively are important soft skills. These competencies enable effective development, analysis, and communication of imaging solutions within research or industry teams.

What does an internship computational imaging scientist do?

An Internship Computational Imaging Scientist assists in developing and applying advanced computational techniques to process and analyze images. This role often involves working with large datasets, using programming languages like Python or MATLAB, and collaborating with senior scientists to solve imaging problems in fields such as biomedical imaging, computer vision, or microscopy. Interns may help design algorithms, conduct experiments, and interpret results to support new imaging technologies. The position offers hands-on experience in both research and practical applications of computational imaging.

What types of projects does an internship computational imaging scientist typically work on, and how do these projects contribute to larger team goals?

As an Internship Computational Imaging Scientist, you’ll often work on projects such as developing new image processing algorithms, optimizing data acquisition techniques, or assisting in the design of computational pipelines for imaging applications. These projects are usually part of broader research or product development efforts and directly support the team’s goals of advancing imaging technology and providing actionable insights from complex data. You’ll frequently collaborate with senior scientists, engineers, and sometimes clinicians or product managers, gaining exposure to multidisciplinary teamwork and real-world problem-solving.

What is the difference between Internship Computational Imaging Scientist vs Research Scientist in Computational Imaging?

AspectInternship Computational Imaging ScientistResearch Scientist in Computational Imaging
CredentialsEnrolled in or recent graduate of relevant degree (e.g., computer science, engineering)Advanced degree (Master's or PhD) often preferred
Work EnvironmentInternship programs, collaborative research labs, industry or academiaFull-time research roles in academia, industry, or research institutions
Employer & Industry UsageTech companies, startups, research labs focusing on imaging techUniversities, research institutes, industry R&D departments
Search & Comparison IntentEntry-level, learning-focused roles, internshipsAdvanced research, development, and innovation roles

The main difference is that an Internship Computational Imaging Scientist is an entry-level, temporary position aimed at gaining experience, while a Research Scientist in Computational Imaging is a full-time, often advanced role focused on conducting independent research and development in the field.

More about Internship Computational Imaging Scientist jobs
What cities are hiring for Internship Computational Imaging Scientist jobs? Cities with the most Internship Computational Imaging Scientist job openings:
What are the most commonly searched types of Computational Imaging Scientist jobs? The most popular types of Computational Imaging Scientist jobs are:
What states have the most Internship Computational Imaging Scientist jobs? States with the most job openings for Internship Computational Imaging Scientist jobs include:
What job categories do people searching Internship Computational Imaging Scientist jobs look for? The top searched job categories for Internship Computational Imaging Scientist jobs are:
Infographic showing various Internship Computational Imaging Scientist job openings in the United States as of August 2026, with employment types broken down into 4% As Needed, 69% Full Time, 14% Part Time, and 13% Contract. Highlights an 95% Physical, 1% Hybrid, and 4% Remote job distribution, with an average salary of $111,343 per year, or $53.5 per hour.

Data Scientist, AI for Biomedical Imaging

Novartis

Cambridge, MA

Full-time

Medical, Life, Retirement, PTO

Re-posted just now


Novartis rating

7.5

Company rating: 7.5 out of 10

Based on 17 frontline employees who took The Breakroom Quiz

64th of 86 rated pharmaceutical


Job description

Job Description Summary

The mission of Novartis is to reimagine medicine, and our team advances that mission by applying advanced image analysis, computer vision, and AI methods to early drug discovery. We partner closely with experimental scientists, disease-area teams, data scientists, bioinformatics experts, and platform engineers to extract meaningful biological insight across diverse imaging modalities (high-content screening, custom microscopy platforms) and biological model systems (cellular assays, co-cultures, organoids, tissue models).
To grow this capability, we are seeking a seasoned, innovative, and collaborative data scientist with deep expertise in AI-enabled image analysis to join the Data Science team in Discovery Sciences (DSc) at Novartis Biomedical Research, Cambridge, MA. This role combines hands-on delivery of robust image analysis workflows with advanced AI method development, including biomedical image segmentation, representation learning, foundation models, and scalable deployment. The successful candidate will embed within the research community as the team's scientific lead for imaging-AI, partnering directly with wet-lab scientists to translate complex biological questions into rigorous, reproducible, and impactful analysis strategies.


Job Description

Internal Job Title: Senior Expert I/II, Data Science

Position Location: Onsite, Cambridge, MA #LI-Onsite

Role Responsibilities:

  • Lead AI-enabled image analysis strategies for complex biological imaging workflows, acting as the embedded imaging-AI scientific partner working side-by-side with wet-lab scientists to understand emerging assay needs, align approaches with scientific priorities and platform standards,and explain advanced AI concepts in accessible terms.

  • Identify high-impact opportunities where AI can deliver meaningful scientific value and define rigorous benchmarking and evaluation strategies to guide method selection.

  • Develop, validate, and deploy robust image analysis algorithms to characterize cellular, organoid, tissue, and other complex biological phenotypes in high-throughput and high-content imaging data, generating reproducible outputs that support decision-making in drug discovery projects.

  • Drive adoption of advanced AI methods for imaging, including deep learning, vision foundation models, embedding-based phenotyping, segmentation, classification, and multimodal integration, translating state-of-the-art methods into practical, validated workflows that augment expert review and enable scalable interpretation of large, high-dimensional datasets.

  • Contribute to scalable, reusable image analysis workflows in partnership with other data scientists, data engineering, and platform teams, championing best practices across the workflow lifecycle.

Essential Requirements:

  • PhD in computer science, AI, machine learning, biomedical image analysis, computational imaging, data science, or a related quantitative field, with 3+ years of applied experience in AI for bioimaging and computer vision.

  • Demonstrated experience developing and validating image analysis algorithms for biological, biomedical, or pharmaceutical research applications, with practical experience in image segmentation, feature extraction, phenotypic profiling, object classification, or representation learning applied to high-content or high-throughput imaging data.

  • Practical expertise in designing benchmarking and evaluation strategies to compare image analysis methods and guide rigorous, evidence-based model selection.

  • Ability to work effectively in Linux-based high-performance computing, cloud, or large-scale data processing environments, with a strong commitment to reproducible research, version control, testing, and data provenance.

  • Strong proficiency in Python and the scientific deep learning stack (e.g., PyTorch, Hugging Face, Lightning, MONAI), along with hands-on experience using image analysis tools such as scikit-image, OpenCV, napari, Cellpose, StarDist, InstanSeg, and OME-Zarr.

  • Self-motivated experienced contributor who thrives in a collaborative, multidisciplinary environment with biologists, imaging scientists, software engineers, and bioinformatics partners, working with appropriate independence and helping shape project direction through both technical expertise and scientific judgment.

  • Excellent scientific communication and stakeholder engagement skills, including the ability to explain complex AI and image analysis concepts to experimental scientists, project teams, engineers, and non-technical audiences.

Desirable Requirements:

  • Experience developing or adapting foundation models, self-supervised learning approaches, multimodal AI models, or embedding-based analysis methods (e.g., DINO, CLIP, SAM) for biological imaging data.

  • Familiarity with the drug discovery pipeline, phenotypic screening, translational biology models, or pharmaceutical research processes.

  • Demonstrated success in turning project-specific solutions into reusable, scalable workflows or standardized analysis products integrated into enterprise platforms, production pipelines, or user-facing tools.

  • Track record of scientific publication, conference presentations, open-source contributions, or internal technical leadership in AI, computer vision, biomedical image analysis, or related fields.

  • Familiarity with agentic coding tools and AI-assisted development workflows (e.g., Claude Code, Copilot).

Compensation & Benefits:

The salary for this position is expected to range between $126,000 and $234,000 USD annually for Senior Expert I, Data Science, and $138,600 and $257,400 USD annually for Senior Expert II, Data Science. The final salary offered is determined based on factors like, but not limited to, relevant skills andexperience, and upon joining Novartis will be reviewed periodically. Novartis may change the publishedsalary range based on company and market factors.


Your compensation will include a performance-based cash incentive and, depending on the level of therole, eligibility to be considered for annual equity awards.


US-based eligible employees will receive a comprehensive benefits package that includes health, life anddisability benefits, a 401(k) with company contribution and match, and a variety of other benefits. Inaddition, employees are eligible for a generous time off package including vacation, personal days,holidays and other leaves.


To learn more about the culture, rewards and benefits we offer our people click here.


EEO Statement:

The Novartis Group of Companies are Equal Opportunity Employers. We do not discriminate in recruitment, hiring, training, promotion or other employment practices for reasons of race, color, religion, sex, national origin, age, sexual orientation, gender identity or expression, marital or veteran status, disability, or any other legally protected status.


Accessibility and reasonable accommodations

The Novartis Group of Companies are committed to working with and providing reasonable accommodation to individuals with disabilities. If, because of a medical condition or disability, you need a reasonable accommodation for any part of the application process, or to perform the essential functions of a position, please send an e-mail to us.reasonableaccommodations@novartis.com or call +1(877)395-2339 and let us know the nature of your request and your contact information. Please include the job requisition number in your message.


Salary Range

$126,000.00 - $234,000.00


Skills Desired

Artificial Intelligence (AI), Biostatistics, Change Management, Curious Mindset, Data Governance, Data Literacy, Data Quality, Data Science, Data Visualization, Deep Learning, Graph Algorithms, Learning Agility, Machine Learning (ML), Machine Learning Algorithms, Python (Programming Language), Stakeholder Engagement, Statistical Analysis, Time Series Analysis

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