Top 2 Computer Scientist Job Jobs in Fort Lee, VA

Summer Research Assistant – Image Analysis & Digital Pathology
Virginia Commonwealth University —Richmond, VA
- $15 an hour
- Part-time
- Computer science
- Computer Science
- University

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Summer Research Assistant – Image Analysis & Digital Pathology
Virginia Commonwealth University —Richmond, VA
Develop, train, and evaluate machine learning and deep learning models for biomedical image analysis tasks (classification, segmentation, object detection).
$15 an hour

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Summer Research Assistant – Image Analysis & Digital Pathology

Virginia Commonwealth University -
Richmond, VA

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Job Details

Part-time | Seasonal
$15 an hour

Qualifications

Data structures
Bachelor's degree

Full Job Description

45002119

907 Floyd Avenue, MCV Main Campus, Virginia, United States, 23284

Research

Hourly/Wage

School Of Medicine MBU

Advertising Summary:

Unit: School Of Medicine MBU

Department: Cellular and Genetic Medicine

Department Summary:

The Department of Cell, Molecular, and Genetics in Medicine (CMGM) at Virginia Commonwealth University is seeking a motivated undergraduate research assistant with a Computer Science background to support an active research program in computational and digital pathology. The student will work closely with faculty, postdoctoral researchers, and graduate students to develop and apply image analysis and machine learning pipelines on whole-slide pathology images, immunohistochemistry, and other biomedical imaging datasets. This is an excellent opportunity for a CS undergraduate to gain hands-on experience at the intersection of artificial intelligence and biomedical research and to contribute directly to ongoing peer-reviewed studies.

Duties & Responsibilities:

Develop, train, and evaluate machine learning and deep learning models for biomedical image analysis tasks (classification, segmentation, object detection).

Implement and adapt digital pathology pipelines for whole-slide image (WSI) processing, including tiling, stain normalization, and feature extraction.

Curate, organize, and annotate image datasets in coordination with pathologists and other domain experts.

Write clean, well-documented Python code using standard ML and computer vision libraries (PyTorch, TensorFlow, scikit-learn, OpenCV, OpenSlide, QuPath).

Maintain reproducible experiments using version control (Git) and notebook-based workflows (Jupyter).

Participate in lab meetings, present progress and findings, and assist in preparing figures and supplementary materials for manuscripts.

Follow all data handling, privacy, and IRB guidance applicable to patient-derived imaging data.

Qualifications:

Minimum Qualifications

Currently enrolled in (or recently completed) a Bachelor's degree program in Computer Science or a closely related discipline (Data Science, Computer Engineering, Applied Mathematics).

Coursework or demonstrated project experience in programming (Python preferred), data structures, and at least one machine learning or computer vision course/project.

Ability to commit consistent weekly hours throughout the semester.

Demonstrated ability to work in and foster an environment of respect, professionalism and civility with a population of faculty, staff, and students from all backgrounds and experiences, or a commitment to do so as an hourly employee at VCU.

Preferred Qualifications

Hands-on experience with deep learning frameworks (PyTorch or TensorFlow).

Experienced in GPUs, HPC clusters, or cloud computing environments.

Coursework or research experience in biology, bioinformatics, or biomedical engineering is a plus but not required.

Strong academic standing (cumulative GPA 3.0 or higher).

Salary Range: 15.00 per hour

Benefits: All VCU employee types are eligible for a wide array of benefits to support you during your employment at VCU. Consult the benefits website for information on benefits eligibility according to employee type.

FLSA Exemption Status: Non-Exempt

Hours per Week:

Up to 25 hours per week; flexible schedule Monday–Friday between 8:00 AM and 6:00 PM, arranged around the student's course schedule.

Flexible Work Arrangement: Fully Onsite

Contact Information:

Contact Email: [email protected]

Apply Now

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