Computational Scientist - AI/ML for Omics Integration

Confirmed live in the last 24 hours

Axle Informatics

Compensation

$115,000 - $130,000/year

Frederick, MD

On-site

Posted October 22, 2025

Job Description

(ID: 2025-0404)

Axle is a bioscience and information technology company that offers advancements in translational research, biomedical informatics, and data science applications to research centers and healthcare organizations nationally and abroad. With experts in biomedical science, software engineering, and program management, we focus on developing and applying research tools and techniques to empower decision-making and accelerate research discoveries. We work with some of the top research organizations and facilities in the country including multiple institutes at the National Institutes of Health (NIH).

Axle is seeking a Computational Scientist - AI/ML for Omics Integration to develop and apply advanced AI/ML approaches to integrate multi-omics datasets for comprehensive organoid characterization and quality assessment. This position located in Frederick, MD at the Standardized Organoid Model Center will focus on creating computational frameworks that can assess organoid fidelity, predict functional outcomes, and identify optimal culture conditions through sophisticated data integration strategies.

Benefits We Offer:

100% Medical, Dental & Vision Coverage for Employees
Paid Time Off and Paid Holidays
401K match up to 5%
Educational Benefits for Career Growth
Employee Referral Bonus
Flexible Spending Accounts:
- Healthcare (FSA)
- Parking Reimbursement Account (PRK)
- Dependent Care Assistant Program (DCAP)
- Transportation Reimbursement Account (TRN)

Overview:

The Standardized Organoid Model Center is an NIH-funded initiative dedicated to advancing organoid research through the development of validated, reproducible, and well-characterized organoid models. The center brings together interdisciplinary teams of researchers to establish standardized protocols, develop quality control measures, and create resources that will benefit the broader organoid research community.

Responsibilities:

The successful candidate will design and implement machine learning algorithms that integrate diverse omics datasets including genomics, transcriptomics, proteomics, and metabolomics data to create comprehensive organoid characterization profiles.
They will develop predictive models that assess organoid quality and functionality based on molecular signatures and identify biomarkers that correlate with successful organoid development.
The role involves creating computational tools for comparing organoid characteristics across different protocols and laboratories to support standardization efforts.
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