ZipRecruiter

Log In

1

Physics Informed Machine Learning Jobs in Illinois

IVC District No. 321

Physics Teacher

Chillicothe, IL · On-site

... student learning. - Develops lesson plans and instructional materials for subject area, and ... machines, and telephone. Travel Requirements - Travels to school district buildings and ...

Rock Valley College

Adjunct - Physics

Rockford, IL · On-site

... Valley College's chosen Learning Management System (LMS) for all grading, assignments ... Keep informed on the latest research and journal articles. 14. Adhere to Family Education Rights ...

Rock Valley College

Adjunct - Physics

Rockford, IL · On-site

... Valley College's chosen Learning Management System (LMS) for all grading, assignments ... Keep informed on the latest research and journal articles. 14. Adhere to Family Education Rights ...

Deloitte

AI Solutions Architect

Chicago, IL · On-site

$65 - $85.50/hr

Leading sales, solution design, and delivery for artificial intelligence, machine learning ... or Physics, or equivalent experience * 8+ years of experience in product sales, software ...

Akuna Capital

Quantitative Researcher

Chicago, IL · On-site

$145K/yr

... machine learning or related area * BS/MS/PhD degree in a technical field - Engineering, Computer Science, Math, Physics, or similar * Proven research background in academic or professional ...

Akuna Capital

Quantitative Researcher

Chicago, IL · On-site

$145K/yr

... machine learning or related area * BS/MS/PhD degree in a technical field - Engineering, Computer Science, Math, Physics, or similar * Proven research background in academic or professional ...

Caterpillar

Data Science/Analyst

Chicago, IL · On-site

Develop models to power asset management solutions for customers and dealers using machine learning, deep learning, and statistics-based/physics-based analytics techniques on time-series sensor data ...

next page

Showing results 1-20

All JobsPhysics Informed Machine Learning JobsPhysics Informed Machine Learning Jobs in Illinois

Physics Informed Machine Learning information

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 are popular job titles related to Physics Informed Machine Learning jobs in Illinois? For Physics Informed Machine Learning jobs in Illinois, the most frequently searched job titles are:
What job categories do people searching Physics Informed Machine Learning jobs in Illinois look for? The top searched job categories for Physics Informed Machine Learning jobs in Illinois are:
What cities in Illinois are hiring for Physics Informed Machine Learning jobs? Cities in Illinois with the most Physics Informed Machine Learning job openings:
Physics Informed Machine Learning jobs near you
Infographic showing various Physics Informed Machine Learning job openings in Illinois as of July 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.
Postdoctoral Appointee - Building Agentic AI Platform for X-ray Science

Postdoctoral Appointee - Building Agentic AI Platform for X-ray Science

Argonne National Laboratory

Lemont, IL

$72K - $121K/yr

Full-time

Re-posted 2 days ago

Job description

We are seeking a highly motivated and creative Postdoctoral Researcher to join the X-ray Science Division (XSD) at Argonne National Laboratory. The successful candidate will develop an AI-enabled platform for X-ray absorption spectroscopy by integrating LLMs, scientific machine learning, physics-aware workflows, and strong computational chemistry/electronic-structure expertise. The researcher will work with a multidisciplinary team to advance agentic AI tools for simulation, interpretation, data analysis, and scientific discovery. The appointment is expected to last two years and the contract is extended yearly.

Position Requirements

  • A recent PhD (within 5 years) in computational chemistry, chemistry, materials science, physics, computational science, computer science, engineering, or a related field.

  • Strong computational chemistry background in atomistic simulations, electronic-structure theory, DFT, structure-property relationships, and interpretation of simulation results.

  • Hands-on experience with DFT or electronic-structure codes such as VASP, Quantum ESPRESSO, CP2K, ABINIT, GPAW, Gaussian, ORCA, Q-Chem, or related packages.

  • Strong materials science or chemistry domain knowledge, such as bonding, defects, catalysis, batteries, solid-state chemistry, molecular systems, or related materials classes.

  • Strong Python skills and familiarity with LLM APIs, agent frameworks , PyTorch, and the Python scientific stack (e.g., numpy, pandas, scikit-learn). Passion for front-end development and web-based applications, back-end services and API design (e.g., FastAPI, Flask), and deploying applications in local or cloud environments.

  • Experience with complex scientific datasets and reproducible analysis or simulation workflows.

  • Effective written and oral communications skills.

  • Demonstrated ability to work both independently and collaboratively in a multidisciplinary environment.

  • Commitment to Argonne's Core Values: Impact, Safety, Respect, Integrity, and Teamwork.

Preferred Knowledge, Skills, and Experience

  • Experience with X-ray absorption spectroscopy theory, modelling, and interpretation, including XANES/EXAFS.

  • Hands-on experience with XAS simulation packages such as FEFF, OCEAN, FDMNES, XSpectra, or exciting.

  • Experience comparing simulated and experimental XAS/XAFS spectra.

  • Experience with high-throughput spectroscopy workflows, HPC, synchrotron datasets, or physics-informed AI.

**Please include a cover letterthat briefly describes relevant simulation, chemistry, AI/ML, and XAS experience; include code links if available.**

Job Family

Postdoctoral

Job Profile

Postdoctoral Appointee

Worker Type

Long-Term (Fixed Term)

Time Type

Full timeThe expected hiring range for this position is $72,879.00-$121,465.00.

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