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Physics Informed Neural Network Jobs (NOW HIRING)

NVIDIA Corporation

... compute, networking, and orchestration.* Solid written and oral communication skills and ... physics-informed neural networks).* Background with NVIDIA Omniverse, OpenUSD, and digital-twin ...

Saint Louis University

Post Doctoral Fellow

Fargo, ND

$48K - $65K/yr

Integrate Physics-Informed Neural Networks or Reinforcement Learning to create realistic human movement and interactive social behaviors within XR. * Instructional Leadership: Assist in teaching and ...

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How much do physics informed neural network jobs pay per hour?

As of Sep 10, 2026, the average hourly pay for physics informed neural network 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 physics informed neural network?

A Physics Informed Neural Network (PINN) is a type of machine learning model that incorporates physical laws, typically expressed as partial differential equations, into the training process of neural networks. By embedding these physical constraints, PINNs can solve forward and inverse problems in engineering and science more accurately and efficiently, even with limited data. They are especially useful for modeling complex systems where traditional data-driven approaches might fail to generalize or respect fundamental physical principles.

What are the key skills and qualifications needed to thrive as a physics informed neural network researcher?

To thrive as a Physics-Informed Neural Network (PINN) Researcher, you need a strong background in applied mathematics, physics, and deep learning, typically supported by an advanced degree in a related field. Proficiency with programming languages such as Python, machine learning libraries (e.g., TensorFlow or PyTorch), and experience with scientific computing tools are essential. Strong analytical thinking, problem-solving skills, and effective communication help researchers interpret results and collaborate with interdisciplinary teams. These skills and qualities are critical for developing accurate models that integrate physical laws with data-driven methods, advancing scientific discovery.

What are some common challenges faced when implementing physics informed neural networks in real-world projects?

Implementing PINNs often involves challenges such as integrating complex physical laws into neural network architectures and ensuring that the model accurately balances data-driven learning with physical constraints. Additionally, training can be computationally intensive, especially when dealing with high-dimensional or stiff differential equations. Collaboration with domain experts—such as physicists or engineers—is typically necessary to correctly formulate the governing equations and interpret results. Despite these challenges, working on PINNs provides opportunities to contribute to cutting-edge applications in engineering, climate modeling, and scientific computing.

What is the difference between Physics Informed Neural Network vs Data Scientist?

AspectPhysics Informed Neural NetworkData Scientist
Required credentialsBackground in machine learning, physics, or engineering; often advanced degreesStatistics, computer science, or related fields; often advanced degrees
Work environmentResearch labs, academia, or tech companies focusing on modeling physical systemsBusiness, tech firms, or consulting firms analyzing data for insights
Industry usageEngineering, scientific research, simulation modelingFinance, marketing, healthcare, tech
Common search intentUnderstanding specialized AI models for physical systemsAnalyzing data patterns and extracting insights

Physics Informed Neural Networks are specialized AI models integrating physical laws into machine learning, primarily used in scientific and engineering contexts. Data Scientists focus on analyzing data to inform business decisions across various industries. While both roles involve machine learning, their applications and environments differ significantly.

What do physics-informed neural networks do?

Physics-informed neural networks (PINNs) are models that incorporate physical laws and equations into their training process to solve complex scientific and engineering problems. They are used to simulate systems governed by differential equations, improve predictive accuracy, and reduce the need for large datasets, often requiring knowledge of programming and machine learning tools. PINNs are valuable in fields like fluid dynamics, material science, and climate modeling.

What other helpful pages are available for Physics Informed Neural Network?

Other pages related to Physics Informed Neural Network:

Infographic showing various Physics Informed Neural Network job openings in the United States as of September 2026, with employment types broken down into 87% Full Time, and 13% Part Time. Highlights an 87% In-person, and 13% Hybrid job distribution, with an average salary of $41,731 per year, or $20.1 per hour.

Staff Development Engineer IV - Engineering Data Scientist and Digital Twin Specialist

Plymouth, MN • On-site

Daikin Applied
Manufacturing • 1 - 5K employees
Learn more about Daikin Appliedexternal

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 20 days ago

Job description

Join the world's largest HVAC company, named by Forbes as one of America's Best-In-State Employers 2025!
Staff Development Engineer IV - Engineering Data Scientist & Digital Twin Specialist- Plymouth, MN - Hybrid
Daikin Applied is seeking an Engineering Data Scientist & Digital Twin Specialist with a strong focus on Reduced Order Modeling (ROM). In this role, you will bridge the gap between high-fidelity 3D physic-based (FEA/CFD), 1D system performance simulations, lab, and real-time operational data. You will build, validate, and deploy fast-running surrogate models and hybrid digital twins that power predictive maintenance, real-time edge analytics, and automated design optimization for our physical assets and systems.
Come be a part of an exciting journey at Daikin Applied, where innovation and excellence drive our every endeavor!
Location: Hybrid - Plymouth, MN
Your Responsibilities:
  • Reduced Order Modeling (ROM): Develop, calibrate, and validate ROMs from complex 3D/multiphysics simulations (e.g., thermal, structural, fluid dynamics) to accelerate computation speeds by orders of magnitude without losing fidelity.
  • Hybrid Digital Twin Development: Design and implement hybrid digital twins that combine first-principles physical models with machine learning/AI (physics-informed neural networks, surrogate modeling) to mirror real-world asset behavior.
  • Data Integration & Pipelines: Ingest, clean, and utilize high-frequency time-series telemetry and IoT sensor data from physical machinery/assets to continuously update and retrain digital models.
  • Deployment & Scaling: Package and deploy ROMs into production environments, cloud platforms, or real-time edge devices using platforms like Ansys Twin Builder, Siemens Simcenter, or custom Python/C++ frameworks.
  • Cross-Functional Collaboration: Work tightly with domain engineers, software developers, and data engineers to integrate digital twin frameworks into broader enterprise architectures and PLM.
  • Model Validation: Conduct rigorous regression testing, scenario analysis, and test-data correlation to ensure numerical stability and accuracy against physical counterparts.

Your Qualifications:
  • Master's or Ph.D. in Mechanical Engineering, Aerospace Engineering, Computer Science, Applied Mathematics, Data Science, or a related technical discipline
  • 6+ years of industry/research experience in applied machine learning, scientific computing, or physics-based simulation
  • Proven track record of building and deploying Reduced Order Models (ROMs) (e.g., Proper Orthogonal Decomposition (POD), Dynamic Mode Decomposition (DMD), or machine learning surrogates like Gaussian Processes and neural networks)
  • Advanced proficiency in Python (NumPy, PyTorch/TensorFlow, Scikit-learn) and/or C++
  • Familiarity with engineering simulation software suites (e.g., Ansys Twin Builder, Siemens Simcenter, MATLAB/Simulink, or OpenFOAM/FEA tools)
  • Experience with time-series databases, IoT data streams (MQTT, OPC UA), and containerization (Docker, Kubernetes) for model deployment
  • Strong understanding of physical principles (dynamics, thermodynamics, heat transfer, structures, or fluid mechanics) alongside statistical modeling and machine learning
  • Strong communication and presentation skills, with the ability to clearly convey technical concepts to both technical and non-technical audiences
  • Demonstrated ability to lead technical project teams and mentor engineers
  • Knowledge of systems engineering and architecture principles
  • Demonstrated ability to work independently and drive collaboration in a cross-functional, globally distributed environment
  • Understanding of model reuse, simulation governance, and lifecycle management concepts
  • Track record of leading cross-disciplinary simulation initiatives or shaping organizational modeling strategy

Your Preferred Qualifications:
  • Experience with MiL and HiL simulation workflows
  • Experience with machine learning, data analytics, or AI-assisted modeling and automation
  • Background in experimental data acquisition and validation of simulation models using test data
  • Experience with physics-informed machine learning (PINMs) or geometric deep learning
  • Exposure to industrial IoT platforms or 3D real-time visualization frameworks (NVIDIA Omniverse, Unity/Unreal)
  • Knowledge of Model-Based Systems Engineering (MBSE) methodologies
  • Deep understanding of thermodynamic cycle modeling, HVAC&R systems, fluid mechanics, heat transfer fundamentals, oil circulation effects, and both steady-state and dynamic system behavior
  • Extensive experience developing, calibrating, and troubleshooting complex model libraries, parameter databases, and calibration routines

Your Benefits:
Daikin Applied offers the following benefits for this position, subject to applicable eligibility requirements:
  • Multiple medical insurance plan options + dental and vision insurance
  • 401K retirement plan with employer match
  • Paid time off and company paid holidays
  • Paid sick time in accordance with the federal, state and local law
  • Tuition Reimbursement after 6 months of continuous service

Work visa sponsorship is not available for this position
The typical annual base salary for this position ranges from $109,100 - $188,700 plus a 15% bonus in Minnesota. The range displayed represents the pay range for all positions in the job grade which these positions fall. Individual base pay will depend on a wide range of factors including your skills, qualifications, experience, and location.
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If you're looking for an engaging career with growth opportunities in a supportive environment, you'll love a career at Daikin Applied!

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