Bioinformatics Fellow - Hyperspectral Imaging at Nextonic Solutions LLC

, Maryland, United States -

Full Time

Start Date

Immediate

Expiry Date

18 Jan, 26

Salary

0.0

Posted On

20 Oct, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Hyperspectral Imaging, Data Analysis, Machine Learning, Cell Culture, Spectroscopy, Microscopy Techniques, Computational Models, Stem Cell Biology, Organoid Systems, Cellular Metabolism, Biochemistry, Analytical Skills, MATLAB, Python, R

Industry

IT Services and IT Consulting

Description

Nextonic Solutions 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. Overview: The Postdoctoral Researcher in Hyperspectral Imaging will advance the SOM Center's characterization capabilities by developing and implementing hyperspectral imaging approaches for cellular and organoid analysis. This position offers an exceptional opportunity to establish novel methodologies for non-invasive cellular characterization that will contribute significantly to organoid standardization efforts and provide valuable training in cutting-edge imaging technologies. Responsibilities: 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. 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.

Responsibilities

The successful candidate will develop hyperspectral imaging protocols for characterizing cells and design experiments to monitor cellular transitions. They will also develop computational models to distinguish cellular states based on hyperspectral data.