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Inverse Imaging Problems Jobs (NOW HIRING)

MD Anderson

Postdoc Fellow - Imaging Physics

Houston, TX

$46K - $63K/yr

A postdoctoral fellowship position is available in the Department of Imaging Physics in the ... inverse problems, and uncertainty quantification. The fellow will have opportunities to contribute ...

MD Anderson

Postdoc Fellow - Imaging Physics

Houston, TX

$46K - $63K/yr

A postdoctoral fellowship position is available in the Department of Imaging Physics in the ... inverse problems, and uncertainty quantification. The fellow will have opportunities to contribute ...

MD Anderson

Postdoc Fellow - Imaging Physics

Houston, TX

$46K - $63K/yr

A postdoctoral fellowship position is available in the Department of Imaging Physics in the ... inverse problems, and uncertainty quantification. The fellow will have opportunities to contribute ...

Siemens Healthineers

Senior AI/ML Scientist

Princeton, NJ · On-site

$134K - $184K/yr

Applying modern machine learning techniques, including deep learning and generative models, to imaging and inverse problems * Collaborating with cross-functional teams (engineering, product, clinical ...

Howard

Assistant Professor

Locke, NY · On-site

The cluster seeks to advance quantitative frameworks for imaging, inference, prediction, and ... Optimization, statistical learning theory, inverse problems, uncertainty quantification, dynamical ...

Howard University

Assistant Professor

Washington, DC · On-site

The cluster seeks to advance quantitative frameworks for imaging, inference, prediction, and ... Optimization, statistical learning theory, inverse problems, uncertainty quantification, dynamical ...

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How much do inverse imaging problems jobs pay per year?

As of Jun 6, 2026, the average yearly pay for inverse imaging problems in the United States is $74,576.00, according to ZipRecruiter salary data. Most workers in this role earn between $50,000.00 and $83,500.00 per year, depending on experience, location, and employer.

What is the difference between Inverse Imaging Problems vs Image Processing Specialist?

AspectInverse Imaging ProblemsImage Processing Specialist
Required CredentialsTypically requires advanced degrees in applied mathematics, physics, or engineeringOften holds degrees in computer science, electrical engineering, or related fields
Work EnvironmentResearch labs, academic institutions, or specialized imaging companiesMedia companies, tech firms, or healthcare imaging departments
Industry UsageFocused on developing algorithms to reconstruct images from indirect or incomplete dataDesigning and implementing image enhancement, editing, and analysis techniques

Inverse Imaging Problems involve reconstructing images from incomplete or indirect data using complex algorithms, often in research settings. Image Processing Specialists focus on improving and analyzing images through various techniques in commercial or clinical environments. While both roles work with images, their goals, methods, and industries differ significantly.

What are inverse imaging problems?

Inverse imaging problems are a class of mathematical and computational challenges where the goal is to reconstruct an image or signal from incomplete, indirect, or noisy measurements. Common examples include medical imaging (like MRI or CT scans), astronomy, and remote sensing. These problems are 'inverse' because they seek to reverse the process that caused the observed data, often requiring sophisticated algorithms to produce accurate and meaningful reconstructions. Solving inverse imaging problems is crucial for improving image quality and extracting useful information from limited data.

What are the key skills and qualifications needed to thrive as an Inverse Imaging Problems Researcher, and why are they important?

To thrive as an Inverse Imaging Problems Researcher, you need a strong background in mathematics, signal processing, and computational imaging, typically supported by an advanced degree in applied mathematics, electrical engineering, or computer science. Familiarity with programming languages such as Python or MATLAB, as well as experience using machine learning frameworks and optimization toolkits, is essential. Analytical thinking, problem-solving ability, and effective communication are valuable soft skills for collaborating on interdisciplinary research and presenting findings. These skills are crucial for developing innovative solutions to reconstruct images from incomplete or indirect data, advancing both scientific understanding and practical applications.

What are some common challenges faced when working on inverse imaging problems in a professional setting?

Professionals working on inverse imaging problems often encounter challenges such as dealing with noisy or incomplete data, selecting appropriate regularization techniques, and ensuring computational efficiency for large-scale problems. Collaboration with domain experts, such as radiologists or engineers, is crucial to properly interpret results and tailor solutions to specific application needs. Staying updated on advances in optimization algorithms and machine learning methods is also important, as the field evolves rapidly and new tools can significantly improve outcomes.

NIST PREP Postdoc Associate, Inverse Problems and Signal Processing for Thermoreflectance Measure...

Southeastern Universities Research Association

Boulder, CO • On-site

$68K - $93K/yr

Full-time

Posted 25 days ago

Job description

This position is part of the National Institute of Standards (NIST) Professional Research Experience (PREP) program. NIST recognizes that its research staff may wish to collaborate with researchers at academic institutions on specific projects of mutual interest, thus requires that such institutions must be the recipient of a PREP award. The PREP program requires staff from a wide range of backgrounds to work on scientific research in many areas. Employees in this position will perform technical work that underpins the scientific research of the collaboration.
Research title: Inverse Problems and Signal Processing for Thermoreflectance Measurements
The work will entail:This postdoctoral position entails developing new analysis techniques and inversion methods related to the thermal properties of computing devices, as probed with through thermoreflectance imaging. The position requires basic knowledge of classical and/or data-driven inverse problems, familiarity with mathematical modeling with differential or integral equations, and experience applying statistical methods to datasets of measured data. Additionally, the applicant should have experience with basic signal processing techniques related to Nyquist sampling theory. The position is aimed at developing new techniques for identifying heat sources in devices that probed with thermoreflectance imaging techniques and in developing rapid measurement techniques for this problem.
Key responsibilities will include but are not limited to:
  • Design, implement, and analyze new algorithms for heat source localization of thermoreflectance images;
  • Design, implement, and analyze new techniques for sampling thermoreflectance images with compressed sensing, adaptive Gaussian processes, or similar sub-Nyquist sampling techniques;
  • Dissemination of research in internal and external publications and presentations;
  • Ensuring that results, protocols, software, and documentation have been archived or otherwise transmitted to the larger organization.

Qualifications
  • A doctoral degree in Applied Mathematics, Electrical Engineering, Computer Science, Engineering, Physics, or a related field.
  • Familiarity with the theory of mathematical inverse problems, forward modeling with differential equations, and a basic understanding of signal processing.
  • Experience with Matlab and/or Python for scientific computing.
  • Experience analyzing datasets of measured physical data.
  • Strong oral and written communication skills.

Privacy Act StatementAuthority: 15 U.S.C. § 278g-1(e)(1) and (e)(3) and 15 U.S.C. § 272(b) and (c)
Purpose: The National Institute for Standards and Technology (NIST) hosts the Professional Research Experience Program (PREP) which is designed to provide valuable laboratory experience and financial assistance to undergraduates, post-bachelor's degree holders, graduate students, master's degree holders, postdocs, and faculty.
PREP is a 5-year cooperative agreement between NIST laboratories and participating PREP Universities to establish a collaborative research relationship between NIST and U.S. institutions of higher education in the following disciplines including (but may not be limited to) biochemistry, biological sciences, chemistry, computer science, engineering, electronics, materials science, mathematics, nanoscale science, neutron science, physical science, physics, and statistics. This collection of information is needed to facilitate administrative functions of the PREP Program.
Routine Uses: NIST will use the information collected to perform the requisite reviews of the applications to determine eligibility, and to meet programmatic requirements. Disclosure of this information is also subject to all the published routine uses as identified in the Privacy Act System of Records Notices: NIST-1: NIST Associates.
Disclosure: Furnishing this information is voluntary. When you submit the form, you are indicating your voluntary consent for NIST to use of the information you submit for the purpose stated.
SURA is an Equal Opportunity Employer. We believe that no one should be discriminated against because of their differences, such as age, disability, ethnicity, gender, gender identity and expression, religion, or sexual orientation. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status, or any other basis as protected by federal, state, or local law.
PREP0003622

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