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Computational Physics Dft Jobs (NOW HIRING)

SES

Computational Materials Scientist

Woburn, MA · On-site +1

What we Need: The SES AI Prometheus team isseeking an exceptional Computational Materials Scientist to combine physics-based simulation (DFT, MD, quantum modeling) with AI-assisted material ...

Xenon7

Research Advisor, Computational Chemistry

We are seeking a highly motivated computational chemist to join our team and apply physics-based ... Familiarity with quantum chemical methods (DFT, ab initio) for electronic structure analysis of ...

Worcester Polytech

Post-Doctoral Fellow

Worcester, MA

$45K - $70K/yr

... in computational materials science with a focus on advanced density functional theory (DFT ... Ph.D. in Materials Science and Engineering, Physics, Chemistry, Mechanical Engineering, or a ...

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How much do computational physics dft jobs pay per year?

As of Jun 3, 2026, the average yearly pay for computational physics dft in the United States is $46,902.00, according to ZipRecruiter salary data. Most workers in this role earn between $43,500.00 and $50,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Computational Physics DFT (Density Functional Theory) specialist, and why are they important?

A strong background in physics, mathematics, and computational modeling, typically with an advanced degree in physics, chemistry, or materials science, is essential for work in computational physics focused on DFT. Proficiency in scientific programming languages (such as Python, Fortran, or C++), experience with DFT simulation packages (like VASP, Quantum ESPRESSO, or Gaussian), and familiarity with high-performance computing environments are often required. Analytical thinking, problem-solving abilities, and effective communication are key soft skills for interpreting complex results and collaborating within multidisciplinary teams. These skills and qualifications are crucial for generating accurate simulations, advancing research, and effectively conveying findings in this highly technical field.

What are some common challenges faced when working with Density Functional Theory (DFT) in computational physics roles?

One of the main challenges in DFT-based computational physics roles is balancing computational cost with the accuracy of results, as more precise calculations often require significantly more resources. Additionally, selecting appropriate exchange-correlation functionals and handling systems with strong electron correlation can be technically demanding. Collaborating closely with experimentalists and other theorists is often necessary to validate models and interpret complex data. Staying updated with the latest methodological advancements in DFT is also vital for ensuring high-quality research outcomes.

What is computational physics DFT?

Computational physics DFT refers to the use of Density Functional Theory (DFT) within the field of computational physics to study the electronic structure of atoms, molecules, and solids. DFT is a quantum mechanical modeling method that allows scientists to calculate properties such as total energy, electronic density, and molecular orbitals efficiently. It is widely used because it provides a good balance between accuracy and computational cost, making it suitable for simulating complex systems in materials science, chemistry, and nanotechnology.

What is the difference between Computational Physics Dft vs Computational Chemistry?

AspectComputational Physics DftComputational Chemistry
Required credentialsPhysics or related degree, knowledge of DFT methodsChemistry or related degree, expertise in molecular modeling
Work environmentResearch labs, academia, industry focusing on physical systemsLaboratories, pharmaceutical companies, research institutions
Industry usageMaterial science, condensed matter physicsDrug design, molecular interactions

Computational Physics Dft and Computational Chemistry both utilize DFT methods, but focus on different systems—physical materials versus molecular interactions. While they share similar credentials and work environments, their applications differ, making each specialized for distinct scientific questions.

Infographic showing various Computational Physics Dft job openings in the United States as of May 2026, with employment types broken down into 6% Internship, 6% As Needed, 82% Full Time, and 6% Contract. Highlights an 92% Physical, 6% Hybrid, and 2% Remote job distribution, with an average salary of $46,902 per year, or $22.5 per hour.
Computational Materials Scientist

Computational Materials Scientist

SES

Woburn, MA • On-site, Remote

Full-time

Medical

Posted yesterday

Job description

SES AI Corp. (NYSE: SES) is dedicated to accelerating the world's energy transition through groundbreaking material discovery and advanced battery management. We are at the forefront of revolutionizing battery creation, pioneering the integration of cutting-edge machine learning into our research and development. Our AI-enhanced, high-energy-density and high-power-density Li-Metal and Li-ion batteries are unique; they are the first in the world to utilize electrolyte materials discovered by AI. This powerful combination of "AI for science" and material engineering enables batteries that can be used across various applications, including transportation (land and air), energy storage, robotics, and drones.
To learn more about us, please visit: www.ses.ai
What We Offer:
  • A highly competitive salary and robust benefits package, including comprehensive health coverage and an attractive equity/stock options program within our NYSE-listed company.
  • The opportunity to contribute directly to a meaningful scientific project-accelerating the global energy transition-with a clear and broad public impact.
  • Work in a dynamic, collaborative, and innovative environment at the intersection of AI and material science, driving the next generation of battery technology.
  • Significant opportunities for professional growth and career development as you work alongside leading experts in AI, R&D, and engineering.
  • Access to state-of-the-art facilities and proprietary technologies are used to discover and deploy AI-enhanced battery solutions.

What we Need:
The SES AI Prometheus team isseeking an exceptional Computational Materials Scientist to combine physics-based simulation (DFT, MD, quantum modeling) with AI-assisted material prediction to generate high-quality training data and accelerate materials discovery. This role is crucial for advancing our understanding of electrochemical energy materials at the atomic level. As a Computational Materials Scientist, you will be a core data-driven modeler responsible for executing and automating complex simulations.
Essential Duties and Responsibilities:
  • Atomistic Modeling & Simulation
  • Conduct and oversee DFT (Density Functional Theory), MD (Molecular Dynamics), and QM (Quantum Mechanics) simulations of battery components, including electrolytes, coatings, and electrodes.
  • Develop and refine ML-enhanced force fields and surrogate models to accelerate simulation time scales and enable multi-scale simulation efforts.
  • Apply expertise in atomistic simulation and quantum modeling to solve key challenges in electrochemical energy materials (e.g., batteries/fuel cells).
  • AI Data Generation & Prediction
  • Generate high-quality, structured simulation data to serve as training sets for AI property prediction models and material screening modules.
  • Contribute to the development of battery domain LLM features and advanced property-prediction models.
  • Automate complex simulation workflows using strong coding practices to enhance efficiency and scalability.
  • Collaboration & Tooling
  • Collaborate with experimental teams, leveraging a hybrid computational + experimental literacy to validate models and drive design iteration.
  • Utilize advanced simulation tools (VASP, Quantum Espresso) and data science libraries (TensorFlow, Pandas) to manage and analyze large datasets.

Education and/or Experience:
  • Education: Ph.D. in Mechanical Engineering, Materials Science, Chemical Engineering, or a closely related computational/physics field.
  • Core Simulation Expertise: Deep and extensive experience in atomistic simulation and quantum modeling, including proficiency with key QM/DFT tools (VASP, Quantum Espresso) and MD simulations.
  • Domain Focus: Strong background in electrochemical energy materials and extensive computational work focused on batteries/fuel cells.
  • Coding Proficiency: Strong coding skills in Python (along with related libraries like Pandas and TensorFlow) for simulation workflow automation and data analysis.
  • ML Application: Experience in developing or utilizing ML-enhanced force fields and surrogate models for materials prediction., or equivalent practical experience.

Preferred Qualifications:
  • LLM Development: Experience in developing battery domain LLM features or property-prediction models.
  • Hybrid Skillset: Demonstrated experience working in a hybrid computational + experimental environment.
  • Tooling Diversity: Familiarity with additional data analysis tools like R, SQL, MATLAB, and time-series forecasting libraries like Prophet.
  • Target Background: Previous experience at national laboratories, XtalPi, Entalpic, or deep battery modeling groups.

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