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Physics Informed Machine Learning Jobs in Auburn, AL

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

As of Jun 10, 2026, the average hourly pay for physics informed machine learning in Auburn, AL is $16.77, according to ZipRecruiter salary data. Most workers in this role earn between $10.43 and $21.30 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.

What cities near Auburn, AL are hiring for Physics Informed Machine Learning jobs? Cities near Auburn, AL with the most Physics Informed Machine Learning job openings:
Infographic showing various Physics Informed Machine Learning job openings in Auburn, AL as of June 2026, with employment types broken down into 1% Locum Tenens, 79% Full Time, 14% Part Time, 3% Contract, and 3% Nights. Highlights an 72% Physical, 3% Hybrid, and 25% Remote job distribution, with an average salary of $34,875 per year, or $16.8 per hour.
Los Alamos National Laboratory Project Researcher

Los Alamos National Laboratory Project Researcher

Auburn University

Auburn University, AL • On-site

$15/hr

Full-time

Posted 24 days ago


Auburn University rating

6.2

Company rating: 6.2 out of 10

Based on 44 frontline employees who took The Breakroom Quiz

467th of 535 rated colleges and universities


Job description

Overview
The RFID Lab at Auburn University is looking for motivated, hardworking, and detail-oriented students from various disciplines to perform Radio Frequency Identification testing and analysis. The student is expected to conduct research and testing for the lab's projects in collaboration with Los Alamos National Laboratory, which includes both running tests and processing the data gathered.
Responsibilities
  • The student is expected to contribute to and design test procedures, work independently on them under strict deadlines, create documentation, and physically conduct experiments.
  • Perform data collection and data analysis to investigate the performance of large-scale Radio Frequency Identification (RFID) deployments.
  • Operate, maintain, and troubleshoot Radio Frequency test equipment.

Qualifications
  • Enrolled student at Auburn University
  • Currently enrolled as an Auburn University graduate student in Electrical or Mechanical Engineering, Physics, or a similar field
  • Strong technical background in one or more of the following areas: signal processing, advanced data analysis, statistics, and machine learning
  • Experience in safe laboratory procedures.
  • Effective verbal and written communication skills
  • Laboratory experience demonstrating attention to detail and commitment to safe work practices.
  • Experience using Microsoft Office Suite products
  • Have an interest in working with Los Alamos National Laboratory

Why Work at Auburn?
Why should YOU consider student employment at Auburn University?
  • Gain valuable work experience! Student employment allows you to explore different interests, identify strengths, and make informed decisions about your future career goals.
  • Build your network! Interacting with faculty, staff, and fellow students can lead to valuable connections and potential mentors.
  • Make a difference! Have the satisfaction of knowing YOU are a part of providing the premier academic experience at Auburn and the life-changing work our students and employees perform.

Equal Opportunity Compliance Statement
It is our policy to provide equal employment and education opportunities for all individuals without regard to race, color, national origin, religion, sex, sexual orientation, gender identity, gender expression, pregnancy, age, disability, protected veteran status, genetic information, or any other classification protected by applicable law. Please visit the Office of Equal Opportunity Compliance's website to learn more.

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