(ID: 2025-0407)
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 Bioinformatics Fellow - AI/ML for Organoid Optimization to join our vibrant team at the National Institutes of Health (NIH) supporting the Standardized Organoid Model Center (SOM).

REQUIRED QUALIFICATIONS

• Candidates must hold a 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 and familiarity with machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn are essential.
• Experience with statistical analysis, optimization algorithms, and data visualization techniques is required.
• The ability to work collaboratively in interdisciplinary teams and communicate complex computational concepts to experimental researchers is necessary.

PREFERRED QUALIFICATIONS

• Previous experience applying machine learning approaches to biological systems or bioengineering applications is highly desirable.
• Familiarity with organoid culture systems, tissue engineering principles, or regenerative medicine research will be considered significant assets.
• Experience with cloud computing platforms, high-performance computing environments, and database management systems is preferred.
• A strong publication record in computational biology or related fields will strengthen candidacy.

TRAINING AND CAREER DEVELOPMENT

This position offers comprehensive training in the application of artificial intelligence to biological research challenges. The researcher will develop expertise in collaborative research methodologies, gain experience in translating computational insights into experimental applications, and contribute to cutting-edge research at the forefront of computational tissue engineering. Opportunities for professional development through manuscript preparation, conference participation, and collaboration with leading researchers in the field will support advancement toward independent research careers in academia or biotechnology industry.
Disclaimer: The above description is meant to illustrate the general nature of work and level of effort being performed by individuals assigned to this position or job description. This is not restricted as a complete list of all skills, responsibilities, duties, and/or assignments required. Individuals may be required to perform duties outside of their position, job description or responsibilities as needed.
The diversity of Axle’s employees is a tremendous asset. We are firmly committed to providing equal opportunity in all aspects of employment and will not tolerate any illegal discrimination or harassment based on age, race, gender, religion, national origin, disability, marital status, covered veteran status, sexual orientation, status with respect to public assistance, and other characteristics protected under state, federal, or local law and to deter those who aid, abet, or induce discrimination or coerce others to discriminate.
Accessibility: If you need an accommodation as part of the employment process please contact: careers@axleinfo.com
This role has a market-competitive salary with an anticipated base compensation range listed below. Actual salaries will vary depending on a candidate’s experience, qualifications, skills, and location.

• The successful candidate will 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.
• They will develop predictive models that can forecast organoid development outcomes and identify protocol modifications that enhance reproducibility and standardization across different laboratory settings.
• A significant component of the research involves creating feedback systems that integrate experimental validation data with computational predictions to iteratively improve protocol optimization algorithms.
• The researcher will collaborate extensively with experimental teams to design validation studies and with data scientists to incorporate diverse data types including omics, imaging, and phenotypic characterization data into comprehensive modeling frameworks.
• The position requires contribution to the development of user-friendly tools and interfaces that enable other researchers to apply optimization models to their specific organoid systems.