Axle is seeking a Bioinformatics Fellow - AI/ML for Organoid Optimization to join their team at the National Institutes of Health (NIH) supporting the Standardized Organoid Model Center (SOM). The goal is to develop sophisticated machine learning models to optimize organoid production protocols, addressing challenges in organoid standardization and reproducibility.
Requirements
- PhD in computer science, bioengineering, applied mathematics, computational biology, or a related quantitative field with demonstrated expertise in AI/ML model development and implementation.
- Strong programming skills in Python.
- Familiarity with machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn.
- Experience with statistical analysis, optimization algorithms, and data visualization techniques.
- Previous experience applying machine learning approaches to biological systems or bioengineering applications.
- Familiarity with organoid culture systems, tissue engineering principles, or regenerative medicine research.
- Experience with cloud computing platforms, high-performance computing environments, and database management systems.
Responsibilities
- Design and implement machine learning algorithms that predict optimal culture conditions for organoid development based on multi-parameter datasets including environmental conditions, growth factor concentrations, timing protocols, and cellular starting materials.
- Develop predictive models that can forecast organoid development outcomes and identify protocol modifications that enhance reproducibility and standardization across different laboratory settings.
- Create feedback systems that integrate experimental validation data with computational predictions to iteratively improve protocol optimization algorithms.
- Collaborate extensively with experimental teams to design validation studies.
- Collaborate with data scientists to incorporate diverse data types including omics, imaging, and phenotypic characterization data into comprehensive modeling frameworks.
- Contribute to the development of user-friendly tools and interfaces that enable other researchers to apply optimization models to their specific organoid systems.
Other
- The ability to work collaboratively in interdisciplinary teams and communicate complex computational concepts to experimental researchers is necessary.
- A strong publication record in computational biology or related fields will strengthen candidacy.
