Unlock your potential with Dassault Systèmes, a global leader in Scientific Software Engineering as an applicative data scientist in Daegu, Korea!

AI Model Development & Selection • Select, adapt, and fine-tune generative AI technologies (diffusion models, GANs, VAEs, and LLMs) to address strategic challenges in generative design and user experience. • Train and qualify AI models, ensuring robust performance, scalability, and strict compliance with company-wide cybersecurity policies, as well as industrial regulations and manufacturing standards. Geometric Algorithm Engineering • Implement and optimize algorithms for 3D mesh processing, computational geometry, and spatial data structures. • Integrate AI-generated outputs into CATIA's applications, ensuring they satisfy engineering and manufacturing requirements such as manufacturability, geometrical constraints and design robustness. Mathematical Optimization & Search • Leverage advanced knowledge in Operations Research, Combinatorial Optimization, and Meta-heuristics (e.g., Simulated Annealing, Adaptive Large Neighborhood Search, MIP) to solve complex design-space search problems. • Combine generative methods with optimization techniques to propose design alternatives that satisfy multiple, often conflicting, engineering constraints. Knowledge Integration & Documentation • Actively learn and incorporate complex CAD/PLM domain knowledge into AI solution design. • Document best practices for selecting, deploying, and integrating AI models within engineering workflows, enabling the broader team to adopt AI technologies effectively. Collaboration & Knowledge Sharing • Work effectively in a global, multi-site R&D environment. • Share knowledge with software engineers, product managers, and domain experts, translating complex AI concepts into actionable engineering insights.

Education • BSc, MSc, or PhD in Data Science, Computer Science, Applied Mathematics, Computational Geometry, or a related field. A focus on 3D technologies is strongly preferred. Technical Expertise AI & Machine Learning • Solid knowledge of machine learning and deep learning, with hands-on experience in Python libraries such as TensorFlow, PyTorch, or equivalents. • Advanced understanding of generative models, including diffusion models, GANs, and VAEs – from theory to deployment. Mathematics & Optimization • Advanced knowledge in mathematics applied to computer science, specifically in: • Operations Research (OR) • Combinatorial Optimization • Meta-heuristics (Simulated Annealing, Adaptive Large Neighborhood Search, MIP solvers, etc.) • Strong background in numerical computation and algorithm design. Geometry & Programming • Strong algorithmic coding skills in both Python and C++. • Proven experience in implementing: • 3D mesh processing • Computational geometry • Spatial data structures (e.g., octrees, bounding volume hierarchies) • Knowledge of object-oriented development is a must (C++ proficiency is essential).

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