AI / ML Intern for Computational Fluid Dynamics (6 m...

Cadence is a pivotal leader in electronic design, building upon more than 30 years of computational software expertise. The company applies its underlying Intelligent System Design strategy to deliver software, hardware and IP that turn design concepts into reality.

Cadence customers are the world's most innovative companies, delivering extraordinary electronic products from chips to boards to systems for the most dynamic market applications including consumer, hyperscale computing, 5G communications, automotive, aerospace industrial and health.

At Cadence, we hire and develop leaders and innovators who want to make an impact on the world of technology.

Job Title: AI/ML Internship for CFD applications in automotive aerodynamics

Location: Brussels, Belgium

Duration: 6 months

Reports to: Software Engineering Director

Job Overview:

Computational Fluid Dynamics (CFD) is critical in engineering but expensive at industrial scales, especially for automotive applications. Emerging machine learning surrogates promise faster predictions; their strengths differ depending on data availability, physics constraints, and geometry complexity.

This AI/ML internship will benchmark data-only and physics-informed approaches for steady RANS external aerodynamics and evaluate whether adding global geometric context to an existing surface prediction workflow improves results.

The goal of this work is to assess whether next‑generation ML surrogates can significantly accelerate external aerodynamics workflows, enabling faster design exploration in the automotive sector.

Job Responsibilities:

You will work with existing high‑quality CFD datasets, internal geometric‑processing tools, and established ML experimentation frameworks to ensure a reproducible and well‑supported workflow.

• Survey the literature on machine‑learning surrogates for steady RANS CFD, including physics‑informed training and multi‑scale geometric context.
• Set up datasets and baselines for a reproducible comparison across identical test cases.
• Implement and train a data‑driven surrogate for surface quantities.
• Prototype physics‑informed training and compare against data‑only setups.
• Evaluate models across clearly defined metrics (accuracy, efficiency, physics compliance, robustness)

Deliverables:

• Literature survey and design rationale.
• Reproducible benchmarking repository (code, configs, scripts).
• Technical report and slides summarizing results and recommendations.

Job Qualifications:

• Master's or final‑year bachelor's in mechanical or aerospace engineering, applied mathematics, computer science, or related
• Strong Python and modern ML frameworks
• Familiarity with CFD fundamentals (Navier–Stokes, RANS, turbulence concepts)
• Experience with geometric data (meshes/point clouds/surfaces)

Additional Skills/Preferences:

• Exposure to physics‑informed ML or operator‑style models

Cadence is committed to equal employment opportunity and employment equity throughout all levels of the organization. We strive to attract a qualified and diverse candidate pool and encourage diversity and inclusion in the workplace.