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

As of Sep 9, 2026, the average hourly pay for machine learning physics postdoc 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 is a machine learning physics postdoc?

A Machine Learning Physics Postdoc is a researcher who applies advanced machine learning techniques to problems in physics. Their work often involves developing algorithms to analyze large datasets, simulate physical systems, or discover patterns within physical phenomena. They typically work in academic or research institutions, collaborating with physicists, computer scientists, and engineers. The role requires expertise in both physics and machine learning, as well as strong programming and analytical skills.

What skills and qualifications are needed to thrive as a machine learning physics postdoc?

To thrive as a Machine Learning Physics Postdoc, you need a PhD in physics or a related field, strong mathematical and programming skills, and experience in applying machine learning to scientific problems. Proficiency with tools like Python, TensorFlow or PyTorch, and high-performance computing environments is typically required. Critical thinking, effective communication, and the ability to work collaboratively in interdisciplinary teams make candidates stand out. These skills are crucial for advancing research, developing innovative solutions, and translating complex physical phenomena into machine learning models.

What are common challenges faced by machine learning physics postdocs when integrating machine learning models with physical theories?

Machine Learning Physics Postdocs often encounter challenges in ensuring that machine learning models adhere to established physical laws and principles. Balancing predictive accuracy with physical interpretability can be demanding, as models may produce results that fit the data but violate known physics. Additionally, sourcing sufficient high-quality data for training and validating models, especially in specialized physics domains, can be a hurdle. Collaborating closely with both physicists and machine learning experts is essential to bridge gaps in domain knowledge and ensure robust, meaningful results.

What are popular job titles related to Machine Learning Physics Postdoc jobs?

For Machine Learning Physics Postdoc jobs, the most frequently searched job titles are:

Infographic showing various Machine Learning Physics Postdoc job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 74% Full Time, 23% Part Time, and 1% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $41,731 per year, or $20.1 per hour.

Post-Doctoral Fellow in Acoustics AF7772

Stillwater, OK • On-site

The OSU/A&M System
See more jobs at The OSU/A&M Systemexternal

$46K - $62K/yr

Full-time, Part-time

Posted 8 days ago

Job description

Post-Doctoral Fellow - Acoustics

Position Summary:

The School of Mechanical and Aerospace Engineering at Oklahoma State University invites applications for a part-time Post-Doctoral Fellow position to join Prof. Yangfan Liu’s research group. The successful candidate will lead interdisciplinary research at the intersection of machine learning, physical and computational acoustics, vibration/noise control, and signal processing.

This is a postdoctoral appointment intended for candidates seeking to contribute and manage ongoing sponsored research projects and supervise students. Ideal candidates will have a strong cross-disciplinary background in acoustics, audio/speech or acoustic signal processing, computational acoustics, and modern machine learning. Key responsibilities include conducting independent research, developing AI-acoustics methodologies with strong practical and translational value, managing projects, contributing to proposals and sponsor reports, preparing high-quality peer-reviewed publications, and mentoring graduate and undergraduate students. The position offers opportunities to collaborate with academic researchers, industrial partners, and students across multiple disciplines.

Responsibilities and Duties

50% – Research

  • Develop machine learning, deep learning, and physics-aware models for acoustics, vibration, NVH, audio/speech, and signal-processing applications.
  • Build surrogate models, inverse-design frameworks, and data-driven optimization methods for acoustic materials, computational acoustics, compressor/vehicle-cabin acoustics, or related vibroacoustic systems.
  • Design and perform experiments to validate developed methodologies and build proof-of-concept demos.
  • Prepare peer-reviewed publications in leading journals and conferences in acoustics, signal processing, machine learning, mechanical engineering, and related fields.
  • Develop reproducible code, analysis pipelines, and technical documentation to support research dissemination and technology transfer.

20% – Research Management

  • Manage day-to-day project tasks to meet sponsor requirements, research milestones, and publication timelines.
  • Coordinate research activities among faculty, students, and external collaborators; organize technical meetings and track deliverables.
  • Provide technical leadership on model development, data analysis, simulation/measurement workflows, and manuscript preparation.

20% – Proposal Development & Professional Service

  • Contribute to research proposals for federal agencies, industry sponsors, and university-industry collaborative programs.
  • Assist with grant management, sponsor communications, technical reporting, and partnership development.
  • Represent the research group at conferences, workshops, sponsor meetings, and professional events.
  • Participate in peer review, professional societies, and academic service activities as appropriate.

10% – Student Mentoring

  • Mentor graduate and undergraduate students in machine learning, acoustics, signal processing, simulation, and data analysis.
  • Guide students in research planning, coding practices, experiment/simulation design, data interpretation, technical writing, and presentations.

Qualifications:

  • Ph.D. in Mechanical or Aerospace Engineering, Acoustics, Electrical and Computer Engineering, Computer Science, Applied Physics, or a closely related field.
  • Strong background in acoustics, vibration/NVH, audio/speech processing, acoustic signal processing, computational acoustics, or related areas.
  • Demonstrated expertise in machine learning or AI methods, such as deep learning, physics-informed or physics-aware learning, surrogate modeling, inverse design, generative models, optimization, or uncertainty-aware modeling.
  • Strong record of scholarly research, including first-author or major-contribution publications in high-quality peer-reviewed journals and conferences.
  • Experience with numerical simulation, experimental/measurement data, acoustic materials/structures, vehicle-cabin acoustics, HAV equipment noise, aerodynamic noise, or speech/audio systems is preferred.
  • Proficiency in research programming and data analysis, preferably using Python and ML frameworks such as PyTorch or TensorFlow; experience with MATLAB, COMSOL, FEM/CFD/acoustic tools, or signal-processing toolchains is a plus.
  • Demonstrated ability to communicate complex technical concepts clearly in reports, manuscripts, presentations, and interdisciplinary team settings.
  • Experience mentoring students, coordinating research tasks, leading collaborative projects, or managing team deliverables is strongly preferred.

SALARY AND BENEFITS:

Salary will be commensurate with education, experience, qualifications, appointment percentage, and contingent on available funding. Benefits, if applicable, will follow Oklahoma State University policies for part-time postdoctoral appointments. Applicants should refer to Oklahoma State University Human Resources for current benefits information.

SPECIAL INSTRUCTIONS TO APPLICANTS:

The process of reviewing applications will begin soon and will continue until a successful candidate is selected.

Interested and qualified candidates should submit a single PDF file including:

  • a cover letter addressing research interests, relevant experience, and skills that fulfill the requirements;
  • a Curriculum Vitae;
  • up to three representative publications.

Questions about the position should be directed to Prof. Yangfan Liu at yangfan.liu@okstate.edu.