Computational Mechanics Master Thesis Internship

Category Interns/Temp Hire Type Intern Job ID 18103 Remote Eligible No Date Posted 07.30.2026

Introduction

We Are: Drive technology innovations that shape the way we live and connect. Our technology drives the Era of Pervasive Intelligence, where smart tech and AI are seamlessly woven into daily life. From self-driving cars and health-monitoring smartwatches to renewable energy systems that efficiently distribute clean power, Synopsys creates high-performance silicon chips that help build a healthier, safer, and more sustainable world.

Internship Experience: At Synopsys, interns dive into real-world projects, gaining hands-on experience while collaborating with our passionate teams worldwide—and having fun in the process! You'll have the freedom to share your ideas, unleash your creativity, and explore your interests. This is your opportunity to bring your solutions to life and work with cutting-edge technology that shapes not only the future of innovation but also your own career path. Join us and start shaping your future today!

Mission Statement: Our mission is to fuel today's innovations and spark tomorrow's creativity. Together, we embrace a growth mindset, empower one another, and collaborate to achieve our shared goals. Every day, we live by our values of Integrity, Excellence, Leadership, and Passion, fostering an inclusive culture where everyone can thrive—both at work and beyond.

What You'll Be Doing:

• Advance the state of the art in trimmed isogeometric analysis (IGA) by developing innovative numerical integration strategies for embedding-based trimmed solid elements
• Perform an in-depth literature review to assess current research trends, challenges, and opportunities in integration point generation for trimmed geometries
• Develop adaptive algorithms that determine efficient integration point layouts based on trimmed element volume fractions while maintaining high numerical accuracy
• Investigate specialized integration concepts for thin-walled structures, including tailored through-thickness integration schemes for nonlinear and elastoplastic analyses
• Prototype and validate new methodologies in Python and translate mature concepts into high-performance Fortran implementations, executing numerical studies and benchmark simulations to evaluate accuracy, robustness, and computational efficiency
• Work closely with leading experts in computational mechanics and finite element technology within the Ansys development team, presenting results to technical stakeholders and documenting your work in a Master's thesis suitable for future research and product development activities

What You'll Need:

• Currently studying towards a Master's degree in Engineering or a related STEM subject with a minimum grade of 1.0–2.0/5.0 (German grading scale)
• Motivated to learn, with strong problem-solving, communication, and time management skills
• Demonstrated ability to write, understand, and present in fluent English
• Knowledge of Continuum Mechanics and Computational Solid Mechanics is preferred
• Preferred: Experience with computer languages, numerical algorithms, fundamentals of high-performance computing, Linux OS, and a general understanding of IT hardware and systems

Key Program Facts:

Program Length: From October 2026 to March 2027

Location: Stuttgart, Germany

Working Model: In office

Full-Time/Part-Time: Full-time

Start Date: October 2026

Equal Opportunity Statement

Synopsys is committed to creating an inclusive workplace and is an equal opportunity employer. We welcome all qualified applicants to apply, regardless of age, color, family or medical leave, gender identity or expression, marital status, disability, race and ethnicity, religion, sexual orientation, or any other characteristic protected by local laws. If you need assistance or a reasonable accommodation during the application process, please reach out to us.

At Synopsys, we want talented people of every background to feel valued and supported to do their best work. Synopsys considers all applicants for employment without regard to race, color, religion, national origin, gender, sexual orientation, age, military veteran status, or disability.