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Physics Informed Neural Networks Jobs (NOW HIRING)

Apple

Machine Learning Engineer

Sunnyvale, CA

$147.40K - $272.10K/yr

Build differentiable simulation and physics-informed machine learning pipelines to analyze and ... deep neural networks. Experience with cutting edge computer vision and machine learning research ...

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

As of May 31, 2026, the average hourly pay for physics informed neural networks 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 Networks job?

A Physics Informed Neural Networks (PINNs) job typically involves developing and applying neural networks that incorporate physical laws as constraints to solve complex scientific and engineering problems. Professionals in this field work on integrating differential equations into deep learning models to improve predictions and reduce the need for large training datasets. These roles are common in fields like fluid dynamics, material science, and climate modeling, where traditional computational methods can be expensive. Individuals in this role often have expertise in machine learning, numerical methods, and domain-specific physics.

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

To thrive in Physics Informed Neural Networks (PINNs), you need a strong background in physics, mathematics, and deep learning frameworks, typically evidenced by advanced degrees in physics, applied mathematics, computer science, or engineering. Experience with programming languages such as Python, and familiarity with libraries like TensorFlow or PyTorch, as well as experience in numerical simulation tools, are commonly required. Strong analytical thinking, problem-solving abilities, and effective communication skills help professionals excel in multidisciplinary teams. These qualifications and soft skills are essential for developing accurate, interpretable models that integrate scientific knowledge with machine learning to solve complex real-world problems.

What are the typical daily tasks involved in a Physics Informed Neural Networks position?

In a Physics Informed Neural Networks role, your daily tasks will often include designing, building, and testing neural network architectures that incorporate physical laws and constraints. You will frequently collaborate with domain experts, such as physicists or engineers, to integrate scientific knowledge into machine learning models and validate the results with real-world data. Regular responsibilities also involve coding, running experiments, analyzing results, and documenting findings for presentation or publication. This collaborative and research-driven environment helps ensure that models are both accurate and physically consistent, and offers opportunities for interdisciplinary learning and skill advancement.
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Physics Informed Neural Networks jobs near you
Electrochemical Engineering Postdoctoral Scholar

Electrochemical Engineering Postdoctoral Scholar

Lawrence Berkeley National Laboratory

Berkeley, CA • On-site

Full-time

Posted 26 days ago

Job description

In this exciting role, you will simulate mathematically fuel cell systems and other related electrochemical devices and their components under both steady state and transient operation in The Energy Conversion Group at Energy Technologies and Systems Division of the Energy Technologies Area. Your work will entail model development, validation, and execution including collaboration with research partners, to verify and explain predicted trends seen in experimental data. The model should identify critical barriers and provide strategies to enable performance optimization and durability mitigation. In addition to cell level modeling, particular emphasis will be on understanding multi-ion transport and durability stressors including structure/function relationships across multiple time and length scales. Additionally, incorporating AI/ML into the multiphysics models via surrogate models or other data-driven methods will be a focus in this position.
We're here for the same mission, to bring science solutions to the world. Join our team and YOU will play a supporting role in our goal to address global challenges! Have a high level of impact and work for an organization associated with 17 Nobel Prizes!
You will:
  • Develop and refine mathematical models to examine multidimensional, multiphysics, transport within an electrochemical cell including its various constitutive layers (e.g., porous transport layers, gas-diffusion and microporous layers, membrane, and catalyst layers).
  • Analyze the results to examine limiting factors in performance as well as identify areas of deficiency in the model and propose new mathematical constructs to deal with them.
  • Pursue microstructural simulations of transient phenomena within single components.
  • Use AI/ML approaches (e.g., physics-informed neural networks) for surrogate model development
  • Validate model activities and comparison of simulation to experimental data for both input parameters and output results.
  • Publish original research in peer-reviewed journals; contribute to scientific publications; present research through talks and posters at conferences, workshops, and multi-investigator meetings.
  • Adhere with the Berkeley Lab and ETA safety requirements.
  • Work on meeting milestones and reporting them to DOE and industrial sponsors
  • Collaborate and work with a team of researchers from diverse backgrounds, and interface with research teams from across industry, academia, and national laboratories

Additional Responsibilities as needed:
  • Work on experimental characterization of cell performance and measurement of component properties.
  • Interact with the LBNL fuel cell and electrochemistry community (with extensive experience in batteries, modeling of batteries and fuel cells, electrode material synthesis, spectroscopy, detailed diagnostics, and cell design) to aid in electrochemistry research.
  • Participate in professional society activities.

We are looking for:
  • PhD in chemical engineering, mechanical engineering, applied physics, or closely related field.
  • Experience with mathematical modeling (i.e., continuum modeling) including in the application of transport phenomena in fuel cells or related devices and at various scales.
  • Familiarity with high performance computing and code development and use including the use of AI/ML and surrogate model development for multiscale analysis
  • Excellent communication skills, both oral and written as well as technical writing.
  • Ability to learn rapidly and integrate new fields to demonstrate creative problem-solving skills
  • Ability and willingness to work in a team environment and collaborate with researchers from various backgrounds
  • Ability to work as an independent researcher with a high level of scientific judgment and initiative.
  • Knowledge of electrochemistry and related diagnostic methods and materials for fuel cells (both low and intermediate temperatures).
  • Knowledge of constitutive relations and continuum theories.

Desired skills/knowledge:
  • Demonstrated ability to take initiative for tackling cross-disciplinary research problems from initiation to meaningful conclusion.
  • Experience with electrochemistry, hydrogen fuel cells, and transport phenomena.
  • Demonstrated strong experience with finite-element methods and Comsol.
  • Familiarity with machine learning and associated big data techniques.

For consideration, please apply with the following application materials:
  • Cover Letter - Describe your interest in this position and the relevance of your background.
  • Curriculum Vitae (CV) or Resume.

Additional information:
  • Appointment type: This is a full-time 2 years, postdoctoral appointment with the possibility of renewal based upon satisfactory job performance, continuing availability of funds and ongoing operational needs. You must have less than 3 years of paid postdoctoral experience. Salary for Postdoctoral positions depends on years of experience post-degree.
  • Salary range: The monthly salary range for this position is $6,891 / mo - $7,609.00 / mo and is expected to start at $6,891 / mo or above. Postdoctoral positions are paid on a step schedule per union contract and salaries will be predetermined based on postdoctoral step rates. Each step represents one full year of completed post-Ph.D. postdoctoral and/or related research experience.
  • Background check: This position is subject to a background check. Any convictions will be evaluated to determine if they directly relate to the responsibilities and requirements of the position. Having a conviction history will not automatically disqualify an applicant from being considered for employment.
  • Work modality: This position will be primarily performed on-site at Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA.
  • Union Represented: This position is represented by a union for collective bargaining purposes.

Want to learn more about working at Berkeley Lab? Please visit: careers.lbl.gov
Equal Employment Opportunity Employer: The foundation of Berkeley Lab is our Stewardship Values: Team Science, Service, Trust, Innovation, and Respect; and we strive to build community with these shared values and commitments. Berkeley Lab is an Equal Opportunity Employer. We heartily welcome applications from all who could contribute to the Lab's mission of leading scientific discovery, excellence, and professionalism. In support of our rich global community, all qualified applicants will be considered for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, protected veteran status, or other protected categories under State and Federal law.
Misconduct Disclosure Requirement: As a condition of employment, the finalist will be required to disclose if they are subject to any final administrative or judicial decisions within the last seven years determining that they committed any misconduct, are currently being investigated for misconduct, left a position during an investigation for alleged misconduct, or have filed an appeal with a previous employer.

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