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Computational Modeling Intern Jobs (NOW HIRING)

Schlumberger

Scientific Computing Intern

Houston, TX · On-site

$14.25 - $19/hr

... of computational tools for Enhanced Geothermal Systems (EGS), with emphasis on proppant ... so the model remains fit-for-purpose for EGS applications while simplifying the numerical ...

The Pennsylvania State University

$18.75 - $24.50/hr

... the Antennas, Computational Electromagnetics, and Propagation Department team of the Applied ... Modify and test EM propagation and antenna models * Implement various methods from published works ...

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Computational Modeling Intern information

What are some common challenges faced by computational modeling interns during their internship?

Computational Modeling Interns often encounter challenges such as adapting to complex simulation software and learning the specific coding standards used by their team. They may also need to balance multiple projects while ensuring the accuracy and reproducibility of their models. Collaboration with researchers and engineers is frequent, so strong communication skills are important to effectively interpret data and incorporate feedback. These challenges provide valuable learning opportunities and help interns develop a strong foundation for a future career in computational modeling.

What are the key skills and qualifications needed to thrive as a computational modeling intern?

To thrive as a Computational Modeling Intern, you generally need a solid background in mathematics, physics, or engineering, along with proficiency in programming languages like Python or MATLAB. Familiarity with simulation software, data analysis tools, and version control systems such as Git is typically required. Strong analytical thinking, problem-solving abilities, and clear communication skills help interns excel when interpreting results and collaborating with research teams. These skills and qualities are essential for efficiently developing, testing, and refining computational models that drive innovation and research progress.

What does a computational modeling intern do?

A Computational Modeling Intern assists in developing and running computer-based simulations to analyze scientific, engineering, or business problems. They work with specialized software and programming languages to build models that predict outcomes, test hypotheses, or optimize processes. Interns often collaborate with experienced researchers or engineers, interpret simulation results, and help document findings. This role provides valuable hands-on experience with computational tools and methodologies used in various industries.

What is the difference between Computational Modeling Intern vs Data Analyst Intern?

AspectComputational Modeling InternData Analyst Intern
Required CredentialsUndergraduate or graduate in STEM, programming skillsUndergraduate or graduate in related field, analytical skills
Work EnvironmentResearch labs, tech companies, academiaBusiness, finance, tech firms, research institutions
Employer & Industry UsageResearch projects, product development, simulationsData analysis, reporting, decision support

Computational Modeling Interns focus on developing and applying computational models and simulations, often requiring programming and mathematical skills. Data Analyst Interns primarily analyze datasets to extract insights, supporting business decisions. While both roles involve data handling, the Modeling Intern emphasizes simulation and algorithm development, whereas the Data Analyst Intern concentrates on data interpretation and reporting.

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Computational Modeling Intern jobs near you
Infographic showing various Computational Modeling Intern job openings in the United States as of August 2026, with employment types broken down into 87% Full Time, 10% Part Time, and 3% Contract. Highlights an 84% Physical, 5% Hybrid, and 11% Remote job distribution.

Ph.D. Graduate Intern - Quantitative Portfolio Risk Analytics

Risk Analytics Company

Cambridge, MA • On-site

Full-time

Re-posted 7 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)