Computational Scientist - AI/ML for Omics Integration

United States·Frederickmid

Data ScienceOtherHealthcareScientist

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Quick Summary

Key Responsibilities

The successful candidate will design and implement machine learning algorithms that integrate diverse omics datasets including genomics, transcriptomics, proteomics,

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

Data ScienceOtherHealthcareScientist

(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.