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Physics Informed Machine Learning Jobs (NOW HIRING)

Quantum Machines

Machine Learning Engineer

Chicago, IL · On-site

In this role, you will work at the intersection of machine learning, quantum physics, and software engineering, translating noisy, non-stationary, safety-critical control problems into ML solutions ...

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How much do physics informed machine learning jobs pay per hour?

As of Jul 11, 2026, the average hourly pay for physics informed machine learning in the United States is $20.06, according to ZipRecruiter salary data. Most workers in this role earn between $12.50 and $25.48 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Physics Informed Machine Learning position, and why are they important?

To thrive in Physics Informed Machine Learning, you need a solid background in physics, strong mathematical and statistical skills, and experience with machine learning algorithms, typically supported by an advanced degree in a relevant field. Proficiency with programming languages like Python, frameworks such as TensorFlow or PyTorch, and familiarity with numerical simulation tools are commonly required. Effective problem-solving, clear communication, and the ability to collaborate with interdisciplinary teams make a significant impact in this role. These capabilities are essential for developing robust, interpretable machine learning models that leverage physical laws to solve complex, real-world problems.

What are the typical challenges faced by professionals working in Physics Informed Machine Learning roles?

Professionals in Physics Informed Machine Learning often encounter challenges integrating complex physical theories with advanced machine learning models, requiring deep domain knowledge and strong technical skills. Balancing model accuracy with computational efficiency and ensuring that models are both interpretable and generalizable can be demanding. Collaboration with domain experts, data scientists, and engineers is common, as projects often span multiple disciplines. Successfully navigating these challenges provides valuable experience and is highly regarded, often leading to further career advancement in research, engineering, or leadership positions.

What is a Physics Informed Machine Learning job?

A Physics Informed Machine Learning (PIML) job involves developing AI models that integrate physics-based principles to improve accuracy, interpretability, and generalization. Professionals in this role use machine learning techniques alongside domain knowledge in physics, engineering, or applied sciences to solve complex problems in areas like fluid dynamics, materials science, and climate modeling. Responsibilities often include designing algorithms, implementing simulations, and validating results against experimental or real-world data. Employers typically seek expertise in deep learning, numerical methods, and programming languages like Python.

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Physics Informed Machine Learning jobs near you
Infographic showing various Physics Informed Machine Learning job openings in the United States as of July 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $41,731 per year, or $20.1 per hour.
Postdoctoral Appointee - AI for Synchrotron Imaging

Postdoctoral Appointee - AI for Synchrotron Imaging

Argonne National Laboratory

Lemont, IL • On-site

$72K - $121K/yr

Full-time

Re-posted 14 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 time
The 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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