Synopsys Inc

Applied Mathematics & Scientific Computing Internship

Synopsys Inc  •  Villeurbanne, FR (Hybrid)  •  4 hours ago
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Job Description


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:

  • Investigate and develop innovative solutions to accelerate orthogonalization and SVD steps that are frequently executed in simulation processes, including modal analyses and data compression
  • Address critical performance bottlenecks in computational workflows to directly contribute to Synopsys' simulation efficiency and success
  • Collaborate with a specialized team responsible for implementing and enhancing core engines that solve eigenproblems in mechanical and multiphysics simulations
  • Work hands-on with industry-leading mechanical solvers, primarily MAPDL and LS-DYNA, to optimize computational performance
  • Apply advanced numerical linear algebra techniques to real-world engineering challenges, making a tangible impact on simulation speed and accuracy


What You'll Need:

  • Currently pursuing an MSc degree in Applied Mathematics, Scientific Computing, or a closely related field
  • Background in numerical linear algebra, including QR decomposition, SVD, and Krylov methods
  • Knowledgeable in probability theory at the level of concentration inequalities
  • Working knowledge of Python, NumPy, and SciPy for scientific computing applications
  • Familiarity with finite-element or CFD (Computational Fluid Dynamics) pipelines is a plus
  • Passion for solving complex computational challenges and optimizing mathematical algorithms


Key Program Facts:

Program Length: 4 months

Location: Lyon, France

Working Model: Hybrid working (flexible balance between working from home and office)

Full-Time/Part-Time: Full-time

Start Date: End of August


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.

Synopsys Inc

About Synopsys Inc

Synopsys is the leader in engineering solutions from silicon to systems, enabling customers to rapidly innovate AI-powered products. We deliver industry-leading silicon design, IP, simulation and analysis solutions, and design services.

We partner closely with our customers across a wide range of industries to maximize their R&D capability and productivity, powering innovation today that ignites the ingenuity of tomorrow.

Industry
IT & Software
Company Size
10,000+ employees
Headquarters
Sunnyvale, California
Year Founded
Unknown
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