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Inverse Problems Jobs (NOW HIRING)

Siemens Healthineers

Senior AI/ML Scientist

Princeton, NJ · On-site

$134K - $184K/yr

Experience with inverse problems, reconstruction, or physics-based modeling * Track record of publications or patents in relevant fields * Experience working in collaborative research or product ...

ExxonMobil

Computational Scientist

Spring, TX · On-site

... inverse problems, data assimilation) while maintaining fit-for-purpose accuracy. • Experience in formulating and solving convex and PDE constrained optimization problems. • Strong proficiency in ...

MD Anderson

Postdoc Fellow - Imaging Physics

Houston, TX · On-site

$46K - $63K/yr

... inverse problems, and uncertainty quantification. The fellow will have opportunities to contribute to ongoing research projects and will be encouraged to explore and develop new areas of research ...

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How much do inverse problems jobs pay per hour?

As of Jun 6, 2026, the average hourly pay for inverse problems in the United States is $33.17, according to ZipRecruiter salary data. Most workers in this role earn between $31.97 and $34.38 per hour, depending on experience, location, and employer.

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

To thrive as an Inverse Problems Specialist, you need a strong background in mathematics, particularly in differential equations, numerical analysis, and statistical inference, usually supported by an advanced degree in applied mathematics, physics, or engineering. Familiarity with programming languages such as MATLAB, Python, or R, and experience using computational modeling and optimization tools, are typically required. Strong analytical thinking, problem-solving abilities, and effective communication skills help in conveying complex concepts and collaborating with interdisciplinary teams. These skills and qualities are essential for developing accurate solutions to complex real-world problems where direct measurement is challenging.

What are some common challenges faced by professionals working in inverse problems, and how can they address them?

Professionals specializing in inverse problems often encounter challenges such as dealing with incomplete or noisy data, model uncertainty, and the inherent ill-posedness of many problems. To address these, practitioners typically employ regularization techniques, collaborate closely with domain experts to refine models, and utilize advanced computational methods for stability and efficiency. Frequent collaboration with interdisciplinary teams is essential, as solutions often require integrating mathematical theory with practical applications in fields like medical imaging, geophysics, or engineering.

What is the difference between Inverse Problems vs Data Analysts?

AspectInverse ProblemsData Analysts
Required credentialsMathematics, applied physics, engineering degreesStatistics, data science, computer science degrees
Work environmentResearch labs, engineering firms, academiaBusiness, finance, healthcare sectors
Employer usageSolving complex scientific and engineering problemsInterpreting data to inform business decisions
Common search intentUnderstanding problem-solving techniques in scientific contextsAnalyzing data trends and insights

Inverse Problems and Data Analysts both work with data and complex calculations, but their focus differs. Inverse Problems primarily involve solving scientific or engineering problems through mathematical modeling, while Data Analysts interpret data to support business decisions. Understanding these differences helps clarify career paths and job expectations in related fields.

What are inverse problems in the context of mathematics and engineering?

Inverse problems are a class of problems where the goal is to determine the underlying causes or parameters that produce observed data or outcomes. Unlike direct problems, which involve predicting results from known causes, inverse problems work backwards from effects to infer the inputs, models, or structures responsible. They are common in fields such as medical imaging, geophysics, and signal processing, where the internal properties of an object or system are deduced from external measurements. Solving inverse problems often requires sophisticated mathematical and computational techniques due to their complexity and potential for multiple solutions.
Infographic showing various Inverse Problems job openings in the United States as of May 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $69,000 per year, or $33.2 per hour.

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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