Axle is seeking a Computational Scientist to develop and apply advanced AI/ML approaches to integrate multi-omics datasets for comprehensive organoid characterization and quality assessment, aiming to create computational frameworks for assessing organoid fidelity, predicting functional outcomes, and identifying optimal culture conditions.
Requirements
- 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.
- Experience with deep learning approaches for biological data, knowledge of systems biology principles, and familiarity with network analysis methods will be considered valuable assets.
Responsibilities
- design and implement machine learning algorithms that integrate diverse omics datasets including genomics, transcriptomics, proteomics, and metabolomics data to create comprehensive organoid characterization profiles.
- develop predictive models that assess organoid quality and functionality based on molecular signatures and identify biomarkers that correlate with successful organoid development.
- 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.
Other
- PhD in computational biology, bioinformatics, computer science, or a related quantitative field
- Previous experience working with organoid datasets or tissue engineering applications is highly desirable.
- Experience with collaborative research projects and manuscript preparation is preferred.
