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Physics Informed Machine Learning Jobs in Memphis, TN

Deloitte

Software Product Architect

Memphis, TN · On-site

... machine learning models for the product in the right way at the right time. Incremental and ... Integrate diverse perspectives to make well-informed decisions that balance feasibility, viability ...

Drax

Heavy Duty Mechanic

Memphis, TN · On-site

$56.82/hr

As a Heavy Duty Mechanic at Drax, you will conduct routine maintenance on machines to ensure the ... A Supportive Team: Work in an environment where continuous learning is encouraged, and your ...

New

Drax

Millwright - Six Month Temporary

Memphis, TN · On-site

$56.54/hr

A Supportive Team: Work in an environment where continuous learning is encouraged, and your ... We're a 'can-do' kind of place, empowering you to make informed decisions and do the right thing.

New

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$5

$19

$24

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

As of May 29, 2026, the average hourly pay for physics informed machine learning in Memphis, TN is $19.49, according to ZipRecruiter salary data. Most workers in this role earn between $12.16 and $24.76 per hour, depending on experience, location, and employer.

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 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.
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