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Theoretical Computer Science Intern Jobs in Boston, MA

IBM

Staff Quantum Algorithm Theorist

Cambridge, MA · On-site

... or theoretical computer science. - Postdoctoral research experience or completion of a graduate program with substantial focus on quantum algorithms, quantum complexity theory, or related areas.

1910

Computational Theoretical Chemist I

Boston, MA · On-site

We hire top 1% talent to join our interdisciplinary team of scientists, engineers, researchers ... Own computational theoretical chemistry programs across therapeutic modalities, disease targets ...

1910

Computational Theoretical Chemist I

Boston, MA · On-site

We hire top 1% talent to join our interdisciplinary team of scientists, engineers, researchers ... Own computational theoretical chemistry programs across therapeutic modalities, disease targets ...

1910

Computational Theoretical Chemist I

Boston, MA · On-site

... science, and software engineering to develop drugs for previously undruggable targets. Role ... Own computational theoretical chemistry programs across therapeutic modalities, disease targets ...

IBM

Quantum algorithm developer

Cambridge, MA · On-site

... or theoretical computer science. - Postdoctoral research experience or completion of a graduate program with substantial focus on quantum algorithms, quantum complexity theory, or related areas.

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Theoretical Computer Science Intern information

What does a theoretical computer science intern do?

A Theoretical Computer Science Intern typically works on fundamental problems in computer science, such as algorithms, computational complexity, cryptography, or data structures. Their work often involves mathematical proofs, designing algorithms, and analyzing their efficiency rather than practical software development. Interns may assist with ongoing research projects, collaborate with senior researchers, and contribute to academic papers or presentations. The goal is to deepen understanding of the theoretical foundations that underpin computer technology.

What types of projects or research topics does a theoretical computer science intern typically work on during their internship?

As a Theoretical Computer Science Intern, you'll often contribute to projects involving algorithm design, computational complexity, cryptography, or formal verification. Interns usually work closely with research scientists or professors, assisting in literature reviews, developing mathematical proofs, and running computational experiments. Collaboration is key, and you may present findings in group meetings or co-author papers. These internships provide an excellent opportunity to deepen your theoretical knowledge while gaining practical experience in a collaborative research environment.

What are the key skills and qualifications needed to thrive as a theoretical computer science intern, and why are they important?

To thrive as a Theoretical Computer Science Intern, you need a solid background in discrete mathematics, algorithms, and computational theory, often supported by ongoing or completed coursework in computer science or mathematics. Familiarity with programming languages like Python or C++, and tools such as LaTeX for documentation, is commonly required. Strong analytical thinking, problem-solving abilities, and effective communication skills help you stand out in collaborative research environments. These skills are crucial for tackling complex theoretical problems, contributing to research projects, and clearly presenting findings.

What is the difference between Theoretical Computer Science Intern vs Software Development Intern?

AspectTheoretical Computer Science InternSoftware Development Intern
Required CredentialsComputer science coursework, strong math skillsProgramming skills, coursework in software engineering
Work EnvironmentResearch labs, academic settings, tech companiesDevelopment teams, tech companies, startups
Industry UsageResearch projects, algorithm development, academiaApplication development, product building, coding

Theoretical Computer Science Interns focus on research, algorithms, and mathematical foundations, often in academic or research settings. Software Development Interns work on coding, building applications, and software projects in industry environments. Both roles require strong technical skills but differ in their focus and work environment.

What cities near Boston, MA are hiring for Theoretical Computer Science Intern jobs?

Cities near Boston, MA with the most Theoretical Computer Science Intern job openings:

Theoretical Computer Science Intern jobs near you

Ph.D. Graduate Intern - Quantitative Portfolio Risk Analytics

Cambridge, MA • On-site

Risk Analytics Company
See more jobs at Risk Analytics Companyexternal

Full-time

Re-posted 4 days ago

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

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)