Research Scientist – Computational Pathology

Huntsman Cancer Institute; Utah; On-site; USD 90,000 - 130,000; Full time

Job summary

The Pan Lab at the Huntsman Cancer Institute (HCI), University of Utah is seeking a highly motivated computational researcher to join our interdisciplinary team.

Our lab develops artificial intelligence (AI) and computational approaches to study cancer using digital pathology and multi-omics data, with an emphasis on translating quantitative tissue phenotypes into biologically and clinically meaningful insights.

Qualifications

• Ph.D. or equivalent doctoral degree or substantial relevant research experience.
• Strong programming skills in Python.
• Experience in Linux/Unix environments and command-line workflows.
• Experience in image processing, computer vision, deep learning, and/or large language models.
• Familiarity with PyTorch or TensorFlow.
• Ability to independently develop, debug, and maintain scientific analysis pipelines.

Responsibilities

• Analyze whole-slide histology images and multimodal data, integrating imaging features with molecular and clinical information.
• Develop and maintain computational analysis pipelines and software tools.
• Collaborate across computational, biological, pathological and clinical disciplines to advance translational cancer research.

Skills

Python

Linux

Image processing

Computer vision

Deep learning

PyTorch

TensorFlow

Scientific programming

Education

PhD or equivalent doctoral degree

Tools

PyTorch

TensorFlow

Job description

Job summary

The Pan Lab at the Huntsman Cancer Institute (HCI), University of Utah is seeking a highly motivated computational researcher to join our interdisciplinary research team. Our lab develops artificial intelligence (AI) and computational approaches to study cancer using digital pathology and multi-omics data, with an emphasis on translating quantitative tissue phenotypes into biologically and clinically meaningful insights.

We are particularly interested in candidates with strong computational skills in computer vision, deep learning, image processing, and scientific programming. The successful candidate will work primarily on computational pathology and multimodal cancer research, including analysis of whole-slide histopathology images and integration of imaging-derived features with spatial, molecular and clinical data.

The Pan Lab is based at Huntsman Cancer Institute and the Department of Oncological Sciences at the University of Utah. Our research lies at the intersection of artificial intelligence, computational pathology, spatial biology, and precision oncology.

The successful candidate will have opportunities to work with large-scale digital pathology and molecular datasets, develop new computational methodology, collaborate across computational, biological, pathological, and clinical disciplines, and contribute to high-impact translational cancer research.

Huntsman Cancer Institute is committed to cancer prevention, care, and survivorship for all communities within the area we serve — which includes Idaho, Montana, Nevada, Utah, and Wyoming — with impact worldwide.

In your cover letter or during your interview process, we invite you to share how your professional experiences have prepared you to serve as a member of our team as we work to reduce the cancer burden experienced by all people and communities. This may include description of your prior experiences related to research, prevention, clinical care, community engagement/outreach, training, administration, or other areas relevant to Huntsman Cancer Institute’s mission and this position.

Learn more about the great benefits of working for University of Utah: benefits.utah.edu

Preferences

Preferred candidates have:

• A Ph.D. or equivalent doctoral degree, or substantial relevant research experience, in Computer Science, Biomedical Engineering, Electrical/Computer Engineering, Bioinformatics, Computational Biology, Data Science, Applied Mathematics, Statistics, or a related quantitative discipline.
• Strong programming skills in Python.
• Experience working in a Linux/Unix environment, including command-line workflows.
• Experience in image processing, computer vision, deep learning, and/or large language models.
• Familiarity with modern deep-learning frameworks such as PyTorch or TensorFlow.
• Ability to independently develop, debug, and maintain scientific analysis pipelines.
• Strong quantitative problem-solving skills and the ability to learn new computational methods independently.
• Effective communication skills and the ability to work collaboratively in an interdisciplinary research environment.

Experience in one or more of the following areas is desirable:

• Computational pathology.
• Deep learning for image segmentation, classification, object detection, representation learning, or related computer-vision tasks.
• Spatial transcriptomics or spatial proteomics, including platforms such as 10x Visium or Xenium.
• Multimodal machine learning and image–omics integration.
• High-performance/GPU computing, workload schedulers, containers, and reproducible computational environments.
• Scientific productivity demonstrated through peer-reviewed publications, conference presentations, software, or other research outputs.

Candidates are not expected to have experience in all of these areas. We particularly value strong computational fundamentals, rigorous problem-solving skills, scientific curiosity, and the ability to independently learn new methods.