Research Scientist
| Company | Autodesk |
|---|---|
| Location | Toronto, ON, Canada |
| Salary | $Not Provided – $Not Provided |
| Type | Full-Time |
| Degrees | Master’s, PhD |
| Experience Level | Senior, Expert or higher |
Requirements
- Advanced degree (Masters or Ph.D.) in Mathematics, Computer Science, Engineering, Physics, or a related discipline
- Excellent knowledge of Numerical Methods and Scientific Computing
- Software development experience in Python and C++ (including Eigen, STL, etc.)
- Publication history in relevant conferences and journals, benchmarking and documenting scientific findings
- Effective collaboration with a multicultural, global team of scientists, engineers and architects
- Excellent written and oral communication skills
- Ability to quickly learn new technologies and adapt to new situations
Responsibilities
- Conceive, plan, develop, and implement scientific research projects to advance scientific knowledge in business-relevant areas
- Communicate and promote the use of research findings throughout Autodesk
- Consult with product teams on the implementation of research findings into products
- Coordinate with team as projects move from research prototype to products/services
- Document and communicate the intent and the results of projects in clear terms to both technical and non-technical team audiences
- Present research findings at conferences and participate in research collaborations with external research institutes and universities
- Publish papers in peer-reviewed scientific journals
- Supervise research interns (undergrad, Masters, PhD or Post-docs)
- Contribute to the technical expertise of the SOS Group
Preferred Qualifications
- Experience in computational engineering physics or computational engineering design
- Experience in data-driven surrogate modelling, reduced-order modelling, or surrogate-based optimization applied to computational engineering design problems
- Experience in implementing numerical methods for solving partial differential equations or gradient-based optimization problems
- Experience in numerical geometry and topology processing
- Experience in geometric representations and data structures (such as NURBS, splines, level sets, surface and volumetric meshes, point clouds, analytical surfaces/solids, functional representations, subdivision surfaces, T-splines, etc.)
- Experience with any of the following highly desirable: Machine learning libraries (PyTorch), 2D and 3D computer graphics programming, Visualization techniques, GUI programming, OpenMP, CUDA, Clang, SIMD, etc., Collaborative development environments and version control systems