ZipRecruiter

Log In

1

Computational Condensed Matter Physics Jobs (NOW HIRING)

University of Utah

$125 - $150/hr

... department's condensed matter program through (i) computational studies of quantum many-body ... D. in Physics or a closely related field. The application must include a cover letter summarizing ...

next page

Showing results 1-20

All JobsScience JobsBiology JobsScientist JobsComputational Condensed Matter Physics Jobs

Computational Condensed Matter Physics information

See salary details

$40

$54

$74

How much do computational condensed matter physics jobs pay per hour?

As of Sep 8, 2026, the average hourly pay for computational condensed matter physics in the United States is $54.93, according to ZipRecruiter salary data. Most workers in this role earn between $46.88 and $73.56 per hour, depending on experience, location, and employer.

What is computational condensed matter physics?

Computational condensed matter physics is a branch of physics that uses computer simulations and numerical methods to study the properties of solids and liquids. Researchers in this field develop and use algorithms to model physical phenomena such as magnetism, superconductivity, and electronic structure at the atomic and molecular levels. This approach allows scientists to explore complex systems that are difficult or impossible to analyze analytically or experimentally, helping to advance our understanding of materials and their behaviors.

What are the key skills and qualifications needed to thrive as a computational condensed matter physicist?

To thrive as a Computational Condensed Matter Physicist, you need a strong background in physics, mathematics, and computational modeling, usually supported by a PhD in physics or a related field. Proficiency with programming languages (such as Python, C++, or Fortran), scientific computing software (like VASP, Quantum ESPRESSO, or MATLAB), and experience with high-performance computing systems are typically required. Analytical thinking, attention to detail, and effective collaboration and communication skills are crucial soft skills that set candidates apart. These competencies are vital for conducting innovative research, interpreting complex data, and efficiently collaborating within interdisciplinary scientific teams.

What are the typical collaborative opportunities for a computational condensed matter physicist within research teams or industry settings?

Computational Condensed Matter Physicists often work closely with experimental physicists, materials scientists, and engineers to interpret and validate simulation results. Collaboration is essential, as theoretical predictions frequently guide experimental investigations or help explain observed phenomena. In both academia and industry, these physicists may be part of interdisciplinary teams, contributing expertise in modeling, software development, and data analysis. Effective communication and teamwork are key, as findings often require integration of computational results with experimental data for publication or product development.

What is the difference between Computational Condensed Matter Physics vs Computational Materials Science?

AspectComputational Condensed Matter PhysicsComputational Materials Science
CredentialsPhysics degrees, research experienceMaterials science or engineering degrees, research experience
Work EnvironmentResearch labs, academia, industry R&DMaterials development, industry labs, academia
Industry UsageFundamental physics, electronic propertiesMaterial design, manufacturing processes

Computational Condensed Matter Physics focuses on understanding the fundamental properties of materials at the atomic level, often emphasizing electronic structure and quantum phenomena. Computational Materials Science, while overlapping, emphasizes the application of simulations to develop new materials and improve manufacturing processes. Both roles require strong physics or materials science backgrounds but differ in their primary focus and industry applications.

More about Computational Condensed Matter Physics jobs

What job categories do people searching Computational Condensed Matter Physics jobs look for?

The top searched job categories for Computational Condensed Matter Physics jobs are:

Computational Condensed Matter Physics jobs near you
Infographic showing various Computational Condensed Matter Physics job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 14% Part Time, and 3% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $114,249 per year, or $54.9 per hour.

Research Scientist, Computational Condensed Matter Physics

Lila Sciences

Cambridge, MA

$126K - $156K/yr

Full-time

Re-posted 11 days ago

Job description

Your Impact at LILA

Your role will involve applying computational condensed matter physics and electronic structure expertise to accelerate materials discovery, optimization, and understanding. You will use first-principles modeling, atomistic simulations, scientific machine learning, and agentic AI systems to investigate complex materials with direct relevance to superconductors, quantum materials, or electronic devices.

You will work at the intersection of physics-based simulation, AI/ML, and autonomous scientific workflows. The focus is on using computational insight to identify promising materials, explain structure-property relationships, guide optimization, and help agents reason over simulation and experimental data in scientifically grounded ways.

This is a hands-on research role for someone who can connect deep condensed matter and electronic structure expertise with practical materials discovery impact. You will collaborate with computational scientists, AI researchers, software engineers, and experimental teams to turn simulations, models, and scientific reasoning into actionable hypotheses and discovery workflows.

What You'll Be Building

  • Apply computational condensed matter physics to materials discovery and optimization.
  • Use computational physics methods (such as electronic structure and phonon calculations) to study quantum materials, superconductors and electronic devices.
  • Connect simulation outputs to experimental observations and develop workflows that close the loop between computation and experiment.
  • Build predictive models from computational and experimental data to guide materials selection and optimization.
  • Analyze simulation and experimental data to generate actionable materials hypotheses.
  • Partner with ML, software, and experimental teams on discovery workflows.
  • Communicate physical insights, model limitations, and recommendations to collaborators.

What You'll Need to Succeed

  • PhD or equivalent experience in Physics, Materials Science, Chemistry, Applied Mathematics, or a related field.
  • Strong foundation in computational condensed matter physics, electronic structure, or atomistic simulation.
  • Deep understanding of electronic-structure theory (quantum chemistry, DFT, beyond-DFT methods, etc.) and their application to electronic, magnetic, or quantum materials.
  • Experience applying first-principles or atomistic methods to materials discovery, optimization, or understanding.
  • Familiarity with superconductors, quantum materials, electronic materials, semiconductors, or device-relevant materials systems.
  • Strong programming skills in Python and scientific computing workflows.

Bonus Points For

  • Experience working with amorphous materials, vibrational properties calculations and advanced electronic structure methods.
  • Experience applying AI/ML to computational materials science or physics-based simulation data.
  • Familiarity with agentic AI systems, autonomous scientific workflows, or simulation-aware agents.
  • Background working with quantum materials, superconductors, semiconductors, or electronic device materials.
  • Experience integrating computational predictions with experimental characterization, device measurements, or closed-loop optimization workflows.
  • Ability to communicate physical insight, uncertainty, and model limitations to cross-functional collaborators.

Apply