Computational Scientist - AI/ML for Omics Integration

United States·Frederickmid

Data ScienceOtherHealthcareScientist

2 views0 saves0 applied

Apply Now

Quick Summary

Overview

(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.

Key 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.

Requirements Summary

Candidates must hold a PhD in computational biology, bioinformatics, computer science, or a related quantitative field with demonstrated experience applying AI/ML methods to biological systems.

Technical Tools

pythona11ydeep-learningmachine-learningproject-managementstatistical-modeling

(ID: 2025-0404)

What We Offer

~3 min read

✓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)

✓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.

✓Collaboration with experimental teams to validate computational predictions and translate findings into actionable protocol improvements will be essential.

✓Candidates must hold a PhD in computational biology, bioinformatics, computer science, or a related quantitative field with demonstrated experience applying AI/ML methods to biological systems.

✓Strong programming skills in Python and R are required, along with experience with machine learning frameworks and statistical analysis packages.

✓Knowledge of multi-omics data integration techniques and experience with biological pathway analysis are necessary.

✓Familiarity with cloud computing platforms and high-performance computing environments is required.

✓Previous experience working with organoid datasets or tissue engineering applications is highly desirable.

✓Experience with deep learning approaches for biological data, knowledge of systems biology principles, and familiarity with network analysis methods will be considered valuable assets.

✓Experience with collaborative research projects and manuscript preparation is preferred.