(ID: 2025-0406)
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 - Hyperspectral Imaging to join our vibrant team at the National Institutes of Health (NIH) supporting the Standardized Organoid Model Center.

REQUIRED QUALIFICATIONS:

• Candidates must possess a PhD in biomedical engineering, physics, chemistry, cell biology, or a related field with demonstrated expertise in analyzing spectroscopy data, with particular preference for hyperspectral imaging experience.
• Strong analytical skills and proficiency in data analysis software such as MATLAB, Python, or R are essential.
• Experience with microscopy techniques and imaging system operation is required, along with the ability to work independently while contributing effectively to collaborative research projects.

PREFERRED QUALIFICATIONS:

• Previous experience working with biological systems, particularly cell culture or tissue engineering applications, will be considered a significant asset.
• Experience with machine learning approaches for spectral data analysis, familiarity with stem cell biology or organoid systems, and knowledge of cellular metabolism and biochemistry are highly desirable.
• Previous postdoctoral experience and a strong publication record in relevant fields will strengthen candidacy.

TRAINING AND CAREER DEVELOPMENT

This position provides comprehensive training in advanced imaging technologies and their application to biological systems. The researcher will gain experience in method development, data analysis, and collaborative research within a multidisciplinary team environment. Opportunities for manuscript preparation, conference presentations, and grant writing will support career advancement toward independent research positions in academia or 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 develop comprehensive hyperspectral imaging protocols for characterizing cells from diverse tissue sources and patient samples, establishing baseline spectral signatures for normal cellular states.
• They will design and execute experiments to monitor cellular transitions during induced pluripotent stem cell differentiation processes, creating spectral libraries that capture the dynamic changes in cellular composition and metabolic states.
• A major component of the research involves developing computational models and machine learning approaches to distinguish cellular states based on hyperspectral data, enabling automated classification of differentiation stages and cellular phenotypes.
• The position requires extensive characterization of organoid systems using hyperspectral imaging techniques, with particular emphasis on comparing organoid spectral signatures to their corresponding primary cell populations to assess fidelity and identify markers of successful organoid development.
• The researcher will collaborate closely with computational teams to integrate hyperspectral data with other omics datasets and will contribute to method development for standardized organoid assessment protocols.