Computational Scientist - AI/ML for Omics Integration

Axle•Frederick, MD

113d•$115,000 - $130,000•Onsite

About The Position

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.

Requirements

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.

Nice To Haves

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.

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