Hyperspectral Imaging Scientist
Listed on 2025-12-02
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Engineering
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IT/Tech
Data Scientist
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Join to apply for the Hyperspectral Imaging Scientist role at Johns Hopkins Applied Physics Laboratory
Johns Hopkins Applied Physics Laboratory provided pay rangeThis range is provided by Johns Hopkins Applied Physics Laboratory. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.
Base pay range$90,000.00/yr - $/yr
Description
Do you see the world in hyperspectral technicolor? Do you have a passion for research and a desire to work on cross-disciplinary teams to develop novel hyperspectral imaging technologies—while contributing to an organization that values impact, innovation, and personal development?
If so, we're looking for someone like you to join our team at APL!
We are seeking a Hyperspectral Imaging Scientist to contribute to the advancement of state-of-the-art hyperspectral capabilities, including end-to-end system simulations, sensor modeling and characterization, algorithm design and implementation, software development, and field validation. As part of this, you will have an opportunity to collaborate within a multidisciplinary team of research engineers, physical scientists, chemists, and material scientists.
As a Hyperspectral Imaging Scientist, you will...
- Design and develop advanced hyperspectral imaging algorithms.
- Execute analysis of hyperspectral imagery from lab experiments and field tests.
- Work on existing tasks and improve existing methodologies to provide enhanced remote sensing capabilities.
- Present research and findings to sponsors, academia, and industry via briefings, conference proceedings, and journal articles.
- Support field integration and testing activities, data analysis, and lab experimentation.
You meet our minimum qualifications for the job if you have...
- A Master's degree in imaging science, spectral science, engineering, computer science, data analytics, or equivalent.
- 5+ years of experience in hyperspectral image processing, algorithm development, subpixel algorithms, statistical signal processing techniques, atmospheric compensation algorithms, temperature emissivity separation algorithms, and radiative transfer theory.
- An interest in broadening technical expertise to areas such as hyperspectral simulations, optical design, formulation chemistry, and orbital mechanics.
- A demonstrated ability to collaborate across a diverse set of technical disciplines, including image processing, computer science, software engineering, chemistry, and material science.
- Proficiency in MATLAB.
- Demonstrated experience in fundamental research and applied research.
- Willingness to travel to local and non-local destinations to support meetings and field tests of imaging systems.
- A results-oriented, self-motivated, and innovative mindset.
- Demonstrated ability to work independently and on a multidisciplinary team.
- Demonstrated excellent written and verbal communication skills to technical and non-technical audiences.
- Hold an active Secret security clearance and can ultimately obtain a TS/SCI level clearance. If selected, you will be subject to a government security clearance investigation and must meet the requirements for access to classified information. Eligibility requirements include U.S. citizenship.
- A PhD in a related field.
- 10+ years of experience in hyperspectral imaging.
- Experience in spectral deconvolution algorithms, hyperspectral image simulations, and hyperspectral sensor performance modeling.
- Experience with sensor APIs.
- Experience with embedded/low Size, Weight, and Power computing devices and/or small UAS sensor integrations.
- Working knowledge of C++ coding language.
- Demonstrated ability to serve as technical lead for a small team of research scientists.
- A wide network within the hyperspectral imaging community.
- Experience in writing proposals.
- An active Top Secret security clearance.
Why Work at APL?
The Johns Hopkins University Applied Physics Laboratory (APL) brings world-class expertise to our nation’s most…
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