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Inverse Problems Jobs (NOW HIRING)

Nvidia

OR

Understanding of physics research workflows, including HPC simulations, numerical solvers, experimental/sensor data, large-scale instruments, inverse problems, and GPU-accelerated scientific ...

Howard University

Assistant Professor

Washington, DC · On-site

Optimization, statistical learning theory, inverse problems, uncertainty quantification, dynamical systems, or computational mathematics relevant to AI. • AI and Data-Driven Modeling in Biomedicine:

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 ...

Array Labs

3D Reconstruction Scientist

Redwood City, CA · On-site

$150K - $300K/yr

You'll work on multi-view geometry, tomography, and large-scale inverse problems, and you'll be responsible for taking methods from prototype to production on real satellite data. The approaches you ...

ExxonMobil

Computational Scientist

Spring, TX

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

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 ...

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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.
Postdoctoral Appointee - AI for Synchrotron Imaging

Postdoctoral Appointee - AI for Synchrotron Imaging

Argonne National Laboratory

Lemont, IL

$72K - $121K/yr

Full-time

Posted 9 days ago

Job description

Position Overview

We are seeking a Postdoctoral Appointee to join the Computational Science and Artificial Intelligence Group in the X-ray Science Division of the Advanced Photon Source (APS) at Argonne National Laboratory to advance learning-enabled imaging methods. This position offers a unique opportunity for candidates with backgrounds in electrical engineering, computer science, applied mathematics, or physics to apply their expertise to challenging problems in computational imaging, while collaborating with leading experts in physics, biology, and environmental science.

Research Context

Soil microbial communities play a fundamental role in carbon and nutrient cycling, yet their spatial organization and interactions have remained difficult to study because of the opacity and complexity of soil. The APS at Argonne National Laboratory is a world-leading synchrotron facility recently upgraded to deliver nanometer-to-micron resolution imaging with dramatically increased X-ray flux. This makes it possible to visualize the interplay of soil structure and microbial life at scales bridging nanometers to millimeters, creating a unique opportunity to investigate how microbial communities are organized and interact within their natural environments.

Your Role

This position focuses on developing learning-enabled imaging methods to guide data collection and analyze synchrotron datasets, spanning the full experimental cycle from real-time X-ray measurements to post-experiment reconstruction:

  • Develop learning-enabled algorithms for 3D reconstruction of noisy and heterogeneous synchrotron datasets.

  • Implement adaptive acquisition strategies that guide beamline measurements in real time to increase efficiency and improve image quality.

  • Advance multimodal analysis methods that align and fuse structural, chemical, and biological signals to construct coherent models of microbial organization across scales.

Success in this role will require creativity in computational imaging, machine learning, and signal processing, as well as close collaboration with experts in computational science, electrical engineering, synchrotron physics, soil microbiology, and environmental chemistry.May be required to perform other duties as assigned.

Position Requirements

  • Ph.D. completed in the past 5 years or soon-to-be completed in Electrical Engineering, Computer Science, Applied Mathematics, Physics, or a related field.

  • Strong expertise in machine learning, computational imaging, computer vision, or signal processing.

  • Proficiency in scientific programming and modern ML frameworks, with the ability to implement and debug research-grade algorithms.

  • Demonstrated ability to work on complex data analysis problems and deliver robust computational solutions.

  • Excellent communication skills and a strong interest in interdisciplinary collaboration.

  • Ability to model Argonne's core values of impact, safety, respect, integrity, and teamwork.

  • Interpersonal skills, oral and written communication skills, and ability to interact with people at all levels both within and outside the laboratory.

Preferred Knowledge, Skills, and Experience

  • Experience with synchrotron or tomographic imaging datasets.

  • Background in inverse problems or physics-informed machine learning.

  • Exposure to scientific imaging applications (for example, biological, environmental, or materials science).

Job Family

Postdoctoral

Job Profile

Postdoctoral Appointee

Worker Type

Long-Term (Fixed Term)

Time Type

Full timeThe expected hiring range for this position is $72,879.00-$121,465.00.

Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package.

Click here to view Argonne employee benefits!

As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law.

Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department.

All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case basis. Please be advised that Argonne positions require upon hire (or may require in the future) for the individual be to obtain a government access authorization that involves additional background check requirements. Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment.

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