ZipRecruiter

Log In

1

Physics Informed Neural Networks Jobs in Chicago, IL

Kemper

Data Scientist

Chicago, IL · Hybrid

... neural networks. * Prior experience working with AWS is a plus. * Some experience with natural ... Mathematics, Statistics, CS, Physics, MIS, Economics, Electrical Engineering, etc. * 2+ years ...

All JobsPhysics Informed Neural Networks JobsPhysics Informed Neural Networks Jobs in Chicago, IL

Physics Informed Neural Networks information

See Chicago, IL salary details

$5

$20

$26

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

As of Aug 8, 2026, the average hourly pay for physics informed neural networks in Chicago, IL is $20.67, according to ZipRecruiter salary data. Most workers in this role earn between $12.88 and $26.25 per hour, depending on experience, location, and employer.

What is a physics informed neural network?

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 physics informed neural networks?

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 does a physics informed neural network do?

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.

What are popular job titles related to Physics Informed Neural Networks jobs in Chicago, IL? For Physics Informed Neural Networks jobs in Chicago, IL, the most frequently searched job titles are:
What job categories do people searching Physics Informed Neural Networks jobs in Chicago, IL look for? The top searched job categories for Physics Informed Neural Networks jobs in Chicago, IL are:
What cities near Chicago, IL are hiring for Physics Informed Neural Networks jobs? Cities near Chicago, IL with the most Physics Informed Neural Networks job openings:
Physics Informed Neural Networks jobs near you
Infographic showing various Physics Informed Neural Networks job openings in Chicago, IL as of August 2026, with employment types broken down into 39% Full Time, 59% Part Time, and 2% Contract. Highlights an 96% Physical, 1% Hybrid, and 3% Remote job distribution, with an average salary of $42,989 per year, or $20.7 per hour.

Postdoctoral Appointee - Synchrotron Studies of Crystal Defects for AI Modeling

Argonne National Laboratory

Lemont, IL

$70K - $117K/yr

Full-time

Re-posted 5 days ago

Job description

The Surface Scattering and Microdiffraction (SSM) group in the X-ray Science Division (XSD) at the Advanced Photon Source (APS), Argonne National Laboratory is seeking Two Postdoctoral Appointees, both focused on multimodal synchrotron characterization of defects and interfaces in oxides and 2D materials. These positions are part of a cross-facility initiative to build an "AlphaFold for Microelectronics"-a physics-informed AI framework that links composition, structure, and operating conditions to defect evolution and functional performance.

The successful candidates will lead experimental campaigns using advanced synchrotron X-ray techniques to generate quantitative, AI-ready datasets that reveal defect-mediated mechanisms governing the stability, adhesion, and transport behavior of thin films and heterointerfaces. The postdoc will lead experimental design, data acquisition, and quantitative reconstruction.

The appointees will work within a highly collaborative team spanning multiple DOE user facilities, who are developing complementary microscopy and AI/ML workflows, ensuring that multimodal datasets (X-ray, electron microscopy, and spectroscopy) are well-aligned and interoperable. These positions offer a unique opportunity to pioneer multimodal, physics-driven synchrotron research that bridges defect dynamics and functionality in emerging microelectronic materials.

Key Responsibilities:

  • Design and perform advanced synchrotron experiments to probe structural, chemical, and dynamic evolution of defects in thin films and heterostructures.

  • Utilize techniques such as Bragg coherent diffraction imaging (BCDI), Laue microdiffraction, ptychographic laminography, and X-ray photon correlation spectroscopy (XPCS) to study strain, dislocation networks, voids, and interfacial morphology.

  • Develop in-situ and operando experiments under electrical, thermal, or mechanical bias to capture real-time defect dynamics.

  • Integrate multimodal datasets and collaborate with AI/ML teams for data fusion, physics-informed model validation, and causal discovery of defect-property relationships.

  • Publish high-impact research results and present findings at national and international conferences.

Position Requirements

  • Ph.D. completed in the past five years or soon-to-be completed in physics, materials science, chemistry, engineering, or a related discipline.

  • Demonstrated expertise in one or more synchrotron X-ray methods such as BCDI, XPCS, ptychography, Laue microdiffraction, or related coherent/imaging techniques.

  • Proven ability to design, conduct, and analyze complex synchrotron experiments.

  • Proficiency in scientific programming (Python, MATLAB, etc.) and quantitative data analysis.

  • Excellent written and oral communication skills.

  • Ability to work effectively in a collaborative, multi-institutional team environment.

  • Ability to model Argonne's core values of impact, safety, respect, integrity, and teamwork.

  • Interpersonal skills, oral and written communication skills, and ability to interact with people at all levels both within and outside the laboratory.

Preferred Knowledge, Skills, and Experience

  • Experience with in-situ or operando measurements under electrical or thermal bias.

  • Familiarity with multimodal data correlation or integration with microscopy/spectroscopy datasets.

  • Awareness of AI/ML data structures and metadata practices for interoperable experimental data.

  • Strong background in materials physics, thin films, or functional oxides/2D materials.

Job Family

Postdoctoral

Job Profile

Postdoctoral Appointee

Worker Type

Long-Term (Fixed Term)

Time Type

Full timeThe expected hiring range for this position is $70,758.00-$117,925.00.

Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package.

Click here to view Argonne employee benefits!

As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law.

Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department.

All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case basis. Please be advised that Argonne positions require upon hire (or may require in the future) for the individual be to obtain a government access authorization that involves additional background check requirements. Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment.

Apply