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Intern Computational Physicist Jobs (NOW HIRING)

The Pennsylvania State University

$18.75 - $24.50/hr

... the Antennas, Computational Electromagnetics, and Propagation Department team of the Applied ... Sufficient Physics, Mathematics, and /or Engineering course work covering basic electromagnetics ...

Pravāh

ML Summer Intern

San Francisco, CA · On-site

$5K - $10K/mo

... computational methods to join us for a summer internship. As an ML Intern at Pravah, you will work ... Currently pursuing a degree in Computer Science, Electrical Engineering, Applied Math, Physics, or ...

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How much do intern computational physicist jobs pay per year?

As of May 31, 2026, the average yearly pay for intern computational physicist in the United States is $168,844.00, according to ZipRecruiter salary data. Most workers in this role earn between $155,500.00 and $182,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an Intern Computational Physicist, and why are they important?

To thrive as an Intern Computational Physicist, you need a solid background in physics, mathematics, and programming, often supported by coursework or a degree in physics, applied mathematics, or a related field. Familiarity with scientific programming languages (such as Python, C++, or MATLAB), numerical simulation software, and version control systems is typically required. Analytical thinking, problem-solving, and effective communication help distinguish candidates in collaborative research environments. These skills are crucial for accurately modeling physical systems, interpreting complex data, and contributing to research projects.

What types of projects and responsibilities can an Intern Computational Physicist expect during their internship?

As an Intern Computational Physicist, you will typically work on projects involving the simulation and modeling of physical systems using computational tools. You may assist with coding, data analysis, and running simulations under the guidance of senior researchers or physicists. Collaboration with multidisciplinary teams, including engineers and software developers, is common, and you'll likely present your findings during team meetings. This role offers a valuable opportunity to gain hands-on experience with industry-standard computational software and to develop both technical and communication skills.

What does an Intern Computational Physicist do?

An Intern Computational Physicist assists in solving complex physical problems using computer simulations, mathematical modeling, and data analysis. They work under the supervision of senior physicists to develop and test algorithms, run simulations, and interpret results for research or industrial projects. Their work often involves programming, analyzing large datasets, and collaborating with multidisciplinary teams to address scientific challenges. This internship provides valuable hands-on experience in applying physics concepts to real-world problems using computational tools.

What is the difference between Intern Computational Physicist vs Intern Data Scientist?

AspectIntern Computational PhysicistIntern Data Scientist
Required CredentialsPhysics degree, programming skillsStatistics, programming, data analysis
Work EnvironmentResearch labs, academia, industry R&DTech companies, finance, healthcare
Industry UsagePhysics research, simulation, modelingData analysis, machine learning, insights

Intern Computational Physicists focus on physics-based modeling and simulations, often in research or industry R&D, requiring physics background and programming skills. Intern Data Scientists analyze data to extract insights, typically in tech or business sectors, with skills in statistics and data analysis. While both roles involve programming, their core focus and industry applications differ significantly.

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Intern Computational Physicist jobs near you

Ph.D. Graduate Intern - Quantitative Portfolio Risk Analytics

Risk Analytics Company

Cambridge, MA • On-site

Full-time

Posted 24 days ago

Job description

Ph.D. Graduate Intern – Quantitative Portfolio Risk Analytics (Cross-Disciplinary)

Position Overview
We are seeking an exceptional Ph.D. graduate student to join our team as a Quantitative Portfolio Risk Analytics Intern. This role focuses on developing and applying advanced analytical methods to understand portfolio risk, market structure, and complex financial systems.
We are intentionally recruiting from cross-disciplinary, research-driven backgrounds. Doctoral candidates from fields such as physics, astrophysics, math, applied mathematics, statistics, engineering, economics, computer science, quantum computing, biotech, and other data-intensive sciences are strongly encouraged to apply—especially those interested in translating rigorous quantitative methods into real-world financial applications.
Key Responsibilities
  • Develop and enhance quantitative models for portfolio risk, including factor-based and statistical approaches 
  • Analyze large, high-dimensional financial datasets to uncover structure, dependencies, and sources of risk 
  • Design and implement analytical tools and pipelines using Python and SQL 
  • Contribute to model validation, backtesting, and performance evaluation 
  • Collaborate with risk, engineering, and data teams to improve model scalability and data infrastructure 
  • Communicate complex quantitative insights through clear visualizations and technical summaries 
  • Apply advanced methodologies from your discipline (e.g., stochastic modeling, optimization, machine learning, or geometric/topological approaches) to improve risk analytics 
Required Qualifications
  • Currently enrolled in a graduate Ph.D. program in a highly quantitative field (e.g., Math, Applied Mathematics, Physics, Astrophysics, Statistics, Computer Science, Engineering, Financial Engineering, Economics, Biotech or other data-driven disciplines) 
  • Strong foundation in probability, statistics, and numerical methods 
  • Proficiency in Python (NumPy, pandas, or similar) and/or SQL 
  • Experience working with large datasets and implementing quantitative models 
  • Ability to think rigorously about complex systems and translate theory into practical solutions 
Preferred Qualifications
  • Familiarity with quantitative finance concepts (e.g., portfolio theory, factor models, volatility modeling, Value-at-Risk) 
  • Experience with scientific computing, optimization, or machine learning 
  • Background or research in cross-disciplinary areas such as: 
    • Statistical physics, complex systems, or network theory 
    • Applied or computational mathematics 
    • Machine learning or probabilistic modeling 
    • Quantum computing or advanced optimization techniques 
    • Topological data analysis or geometric data methods 
  • Prior research, publications, or project work demonstrating advanced quantitative modeling 
What You’ll Gain
  • Exposure to real-world portfolio risk problems at the intersection of finance and advanced analytics 
  • Opportunity to apply cutting-edge academic methods in a production environment 
  • Collaboration with a highly quantitative, cross-disciplinary team 
  • Experience working with large-scale financial data and modern analytics infrastructure 
  • Mentorship and potential pathway to full-time quantitative roles 
Duration & Compensation
  • Internship: Summer 2026, with potential to extend 
  • Paid internship (competitive, based on experience and location)